Most Popular Data Mining Interview Questions and Answers

Last updated on 12th Nov 2021, Blog, Interview Questions

Data mining is a method that companies are working to convert raw data into the useful required information. It is applied for the extraction of patterns and knowledge from large amounts of data. f you are looking for a job related to Data Mining, you need to prepare for the 2020 Data Mining Interview Questions. Every interview is indeed different as per the various job profiles but still, to clear the interview, you need to have a good and precise knowledge of Data Mining. Here, we have prepared the important Data Mining Interview Questions and Answers, which will help you succeed in your interview.

1. What is Data Mining?

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Data Mining refers to extracting or mining knowledge from large amounts of data. In other words, Data mining is the science, art, and technology of discovering large and complex bodies of data in order to discover useful patterns.

2. What are the different tasks of Data Mining?

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The following activities are carried out during data mining:,

• Classification
• Clustering
• Association Rule Discovery
• Sequential Pattern Discovery
• Regression
• Deviation Detection

3. Discuss the Life cycle of Data Mining projects?

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The life cycle of Data mining projects:

• Business understanding: Understanding project objectives from a business perspective, data mining problem definition.
• Data understanding: Initial data collection and understand it.
• Data preparation: Constructing the final data set from raw data.
• Modeling: Select and apply data modeling techniques.
• Evaluation: Evaluate model, decide on further deployment.
• Deployment: Create a report, carry out actions based on new insights.

4. Explain the process of KDD?

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Data mining is treated as a synonym for another popularly used term, Knowledge Discovery from Data, or KDD. Others view data mining as simply an essential step in the process of knowledge discovery, in which intelligent methods are applied in order to extract data patterns.

Knowledge discovery from data consists of the following steps:

• Data cleaning (to remove noise or irrelevant data).
• Data integration (where multiple data sources may be combined).
• Data selection (where data relevant to the analysis task are retrieved from the database).
• Data transformation (where data are transmuted or consolidated into forms appropriate for mining by performing summary or aggregation functions, for sample).
• Data mining (an important process where intelligent methods are applied in order to extract data patterns).
• Pattern evaluation (to identify the fascinating patterns representing knowledge based on some interestingness measures).
• Knowledge presentation (where knowledge representation and visualization techniques are used to present the mined knowledge to the user).

5. What is Classification?

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Classification is the process of finding a set of models (or functions) that describe and distinguish data classes or concepts, for the purpose of being able to use the model to predict the class of objects whose class label is unknown. Classification can be used for predicting the class label of data items. However, in many applications, one may like to calculate some missing or unavailable data values rather than class labels.

6. Explain Evolution and deviation analysis?

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EVOLUTION	DEVIATION ANALYSIS
Data evolution analysis describes and models regularities or trends for objects whose behavior variations over time.	In the analysis of time-related data, it is often required not only to model the general evolutionary trend of the data but also to identify data deviations that occur over time.
Although this may involve discrimination, association, classification, characterization, or clustering of time-related data, distinct features of such an analysis involve time-series data analysis, periodicity pattern matching, and similarity-based data analysis.	Deviations are differences between measured values and corresponding references such as previous values or normative values. A data mining system performing deviation analysis, upon the detection of a set of deviations, may do the following: describe the characteristics of the deviations, try to describe the reason behind them, and suggest actions to bring the deviated values back to their expected values.

7. What is Prediction?

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Prediction can be viewed as the construction and use of a model to assess the class of an unlabeled object, or to measure the value or value ranges of an attribute that a given object is likely to have. In this interpretation, classification and regression are the two major types of prediction problems where classification is used to predict discrete or nominal values, while regression is used to predict incessant or ordered values.

8. Explain the Decision Tree Classifier?

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A Decision tree is a flow chart-like tree structure, where each internal node (non-leaf node) denotes a test on an attribute, each branch represents an outcome of the test and each leaf node (or terminal node) holds a class label. The topmost node of a tree is the root node.

A Decision tree is a classification scheme that generates a tree and a set of rules, representing the model of different classes, from a given data set.

9. What are the advantages of a decision tree classifier?

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• Decision trees are able to produce understandable rules.
• They are able to handle both numerical and categorical attributes.
• They are easy to understand.
• Once a decision tree model has been built, classifying a test record is extremely fast.
• Decision tree depiction is rich enough to represent any discrete value classifier.
• Decision trees can handle datasets that may have errors.
• Decision trees can deal with handling datasets that may have missing values.
• They do not require any prior assumptions. Decision trees are self-explanatory and when compacted they are also easy to follow. That is to say, if the decision tree has a reasonable number of leaves it can be grasped by non-professional users. Furthermore, since decision trees can be converted to a set of rules, this sort of representation is considered comprehensible.

10. Explain Bayesian classification in Data Mining?

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A Bayesian classifier is a statistical classifier. They can predict class membership probabilities, for instance, the probability that a given sample belongs to a particular class. Bayesian classification is created on the Bayes theorem. A simple Bayesian classifier is known as the naive Bayesian classifier to be comparable in performance with decision trees and neural network classifiers. Bayesian classifiers have also displayed high accuracy and speed when applied to large databases.

11. Why Fuzzy logic an important area for Data Mining?

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Rule-based systems for classification have the disadvantage that they involve exact values for continuous attributes. Fuzzy logic is useful for data mining systems performing classification. It provides the benefit of working at a high level of abstraction. In general, the usage of fuzzy logic in rule-based systems involves the following:

Attribute values are changed to fuzzy values.

For a given new sample, more than one fuzzy rule may apply. Every applicable rule contributes a vote for membership in the categories. Typically, the truth values for each projected category are summed.

12. What are Neural networks?

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A neural network is a set of connected input/output units where each connection has a weight associated with it. During the knowledge phase, the network acquires by adjusting the weights to be able to predict the correct class label of the input samples. Neural network learning is also denoted as connectionist learning due to the connections between units. Neural networks involve long training times and are therefore more appropriate for applications where this is feasible. They require a number of parameters that are typically best determined empirically, such as the network topology or “structure”.

13. How Backpropagation Network Works?

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A Backpropagation learns by iteratively processing a set of training samples, comparing the network’s estimate for each sample with the actual known class label. For each training sample, weights are modified to minimize the mean squared error between the network’s prediction and the actual class. These changes are made in the “backward” direction, i.e., from the output layer, through each concealed layer down to the first hidden layer (hence the name backpropagation). Although it is not guaranteed, in general, the weights will finally converge, and the knowledge process stops.

14. What is a Genetic Algorithm?

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Genetic algorithm is a part of evolutionary computing which is a rapidly growing area of artificial intelligence. The genetic algorithm is inspired by Darwin’s theory about evolution. Here the solution to a problem solved by the genetic algorithm is evolved. In a genetic algorithm, a population of strings (called chromosomes or the genotype of the gen me), which encode candidate solutions (called individuals, creatures, or phenotypes) to an optimization problem, is evolved toward better solutions. Traditionally, solutions are represented in the form of binary strings, composed of 0s and 1s, the same way other encoding schemes can also be applied.

15. What is Classification Accuracy?

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Classification accuracy or accuracy of the classifier is determined by the percentage of the test data set examples that are correctly classified. The classification accuracy of a classification tree = (1 – Generalization error).

16. Define Clustering in Data Mining?

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Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.

17. Write a difference between classification and clustering?[IMP]

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Classification	Clustering
Used for supervised need learning	Used for unsupervised learning
Logistic regression, Naive Bayes classifier, Support vector machines, etc.	algorithm, Fuzzy c-means clustering algorithm, Gaussian (EM) clustering algorithm etc

18. Name areas of applications of data mining?

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• Data Mining Applications for Finance
• Healthcare
• Intelligence
• Telecommunication
• Energy
• Retail
• E-commerce
• Supermarkets
• Crime Agencies
• Businesses Benefit from data mining

19. What is Supervised and Unsupervised Learning?

Supervised	Unsupervised
As the name indicates, has the presence of a supervisor as a teacher. Basically supervised learning is when we teach or train the machine using data that is well labeled. Which means some data is already tagged with the correct answer.	The training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance.
After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data	Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

20. What are the issues in data mining?

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• A number of issues that need to be addressed by any serious data mining package
• Uncertainty Handling
• Dealing with Missing Values
• Dealing with Noisy data
• Efficiency of algorithms
• Constraining Knowledge Discovered to only Useful
• Incorporating Domain Knowledge
• Size and Complexity of Data
• Data Selection
• Understandably of Discovered Knowledge: Consistency between Data and Discovered Knowledge.

21. Give an introduction to data mining query language?

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DBQL or Data Mining Query Language proposed by Han, Fu, Wang, et.al. This language works on the DBMiner data mining system. DBQL queries were based on SQL(Structured Query language). We can use this language for databases and data warehouses as well. This query language supports ad hoc and interactive data mining.

22. Differentiate Between Data Mining And Data Warehousing?

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Data mining	Data warehousing
It is the process of finding patterns and correlations within large data sets to identify relationships between data. Data mining tools allow a business organization to predict customer behavior	A data warehouse is designed to support the management decision-making process by providing a platform for data cleaning, data integration, and data consolidation.
It is a technology that aggregates structured data from one or more sources so that it can be compared and analyzed rather than transaction processing.	Data warehouse consolidates data from many sources while ensuring data quality, consistency, and accuracy. Data warehouse improves system performance by separating analytics processing from transnational databases.

23. What is Data Purging?

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The term purging can be defined as Erase or Remove. In the context of data mining, data purging is the process of remove, unnecessary data from the database permanently and cleaning data to maintain its integrity.

24. What Are Cubes?

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A data cube stores data in a summarized version which helps in a faster analysis of data. The data is stored in such a way that it allows reporting easily. E.g. using a data cube A user may want to analyze the weekly, monthly performance of an employee. Here, month and week could be considered as the dimensions of the cube.

25. What are the differences between OLAP And OLTP?

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OLAP	OLTP
Consists of historical data from various Databases.	Consists only of application-oriented day-to-day operational current data.
This data is generally managed by the CEO, MD, GM.	This data is managed by clerks, managers
Only read and rarely write operation	Both read and write operations.

26. Explain Association Algorithm In Data Mining?

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Association analysis is the finding of association rules showing attribute-value conditions that occur frequently together in a given set of data. Association analysis is widely used for a market basket or transaction data analysis. Association rule mining is a significant and exceptionally dynamic area of data mining research. One method of association-based classification, called associative classification, consists of two steps. In the main step, association instructions are generated using a modified version of the standard association rule mining algorithm known as Apriori. The second step constructs a classifier based on the association rules discovered.

27. Explain how to work with data mining algorithms included in SQL server data mining?

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SQL Server data mining offers Data Mining Add-ins for Office 2007 that permits finding the patterns and relationships of the information. This helps in an improved analysis. The Add-in called a Data Mining Client for Excel is utilized to initially prepare information, create models, manage, analyze, results.

28. What is the difference between Data Mining and Data Analysis?

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Data mining	Data analysis
Used to perceive designs in data stored.	Used to arrange and put together raw information in a significant manner
Results extracted from data mining are difficult to interpret.	Results extracted from information analysis are not difficult to interpret

29. Define Tree Pruning?

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When a decision tree is built, many of the branches will reflect anomalies in the training data due to noise or outliers. Tree pruning methods address this problem of overfitting the data. So tree pruning is a technique that removes the overfitting problem. Such methods typically use statistical measures to remove the least reliable branches, generally resulting in faster classification and an improvement in the ability of the tree to correctly classify independent test data. The pruning phase eliminates some of the lower branches and nodes to improve their performance. Processing the pruned tree to improve understandability.

30. Explain the data mines and techniques?

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31. Define Chameleon Method?

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Chameleon is another hierarchical clustering technique that utilizes dynamic modeling. Chameleon is acquainted with recovering the disadvantages of the CURE clustering technique. In this technique, two groups are combined, if the interconnectivity between two clusters is greater than the inter-connectivity between the object inside a cluster/ group.

32. Explain the Issues regarding Classification And Prediction?

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Preparing the data for classification and prediction:

• Data cleaning
• Relevance analysis
• Data transformation
• Comparing classification methods
• Predictive accuracy
• Speed
• Robustness
• Scalability
• Interpretability

33. Explain the use of data mining queries or why data mining queries are more helpful?

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It additionally recovers the insights concerning the individual cases utilized in the model. It incorporates the information which isn’t utilized in the analysis, it holds the model with the assistance of adding new data and performs the task and is cross-verified.

34. What is the difference between univariate, bivariate, and multivariate analysis?

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The main difference between univariate, bivariate, and multivariate investigation are as per the following:

Univariate	Multivariate	Bivariate
A statistical procedure that can be separated depending on the check of factors required at a given instance of time	The analysis of multiple variables is known as multivariate. This analysis is utilized to comprehend the impact of factors on the responses.	This analysis is utilized to discover the distinction between two variables at a time.

35. Describe the study of partial data mining architecture and technology?

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36. What are precision and recall?

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Precision is the most commonly used error metric in the n classification mechanism. Its range is from 0 to 1, where 1 represents 100%.

Recall can be defined as the number of the Actual Positives in our model which has a class label as Positive (True Positive)”. Recall and the true positive rate is totally identical. Here’s the formula for it:

Recall = (True positive)/(True positive + False negative)

37. What are the ideal situations in which t-test or z-test can be used?

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It is a standard practice that a t-test is utilized when there is an example size under 30 attributes and the z-test is viewed as when the example size exceeds 30 by and large.

38. What is the simple difference between standardized and unstandardized coefficients

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Standardized	Unstandardized
In the case of normalized coefficients, they are interpreted dependent on their standard deviation values	The unstandardized coefficient is estimated depending on the real value present in the dataset.

39. How are outliers detected?

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Numerous approaches can be utilized for distinguishing outliers anomalies, but the two most generally utilized techniques are as per the following:

Standard deviation strategy: Here, the value is considered as an outlier if the value is lower or higher than three standard deviations from the mean value.

Box plot technique: Here, a value is viewed as an outlier if it is lesser or higher than 1.5 times the interquartile range (IQR)

40. Why is KNN preferred when determining missing numbers in data?

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K-Nearest Neighbour (KNN) is preferred here because of the fact that KNN can easily approximate the value to be determined based on the values closest to it.

The k-nearest neighbor (K-NN) classifier is taken into account as an example-based classifier, which means that the training documents are used for comparison instead of an exact class illustration, like the class profiles utilized by other classifiers.

41. Explain Pre Pruning and Post pruning approach in Classification?

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Pre Pruning	Post pruning
In the pre pruning approach, a tree is “pruned” by halting its construction early (e.g., by deciding not to further split or partition the subset of training samples at a given node). Upon halting, the node becomes a leaf. The leaf may hold the most frequent class among the subset samples, or the probability distribution of those samples.	The post pruning approach removes branches from a “fully grown” tree. A tree node is pruned by removing its branches. The cost complexity pruning algorithm is an example of the post pruning approach. The pruned node becomes a leaf and is labeled by the most frequent class among its former branches.
There are problems, however, in choosing a proper threshold. High thresholds could result in oversimplified trees, while low thresholds could result in very little simplification.	After generating a set of progressively pruned trees, an independent test set is used to estimate the accuracy of each tree. The decision tree that minimizes the expected error rate is preferred.

42. How can one handle suspicious or missing data in a dataset while performing the analysis?

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If there are any inconsistencies or uncertainty in the data set, a user can proceed to utilize any of the accompanying techniques: Creation of a validation report with insights regarding the data in conversation Escalating something very similar to an experienced Data Analyst to take a look at it and accept a call Replacing the invalid information with a comparing substantial and latest data information Using numerous methodologies together to discover missing values and utilizing approximation estimates if necessary.

43. What is data mining in Excel?

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Mining implies digging, and using Excel for data mining lets you dig for useful information – hidden gems in your data. In this lesson, we’ll define data mining and show how Excel can be a great tool for finding patterns in information.

44. Explain Over-fitting?

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The concept of overfitting is very important in data mining. It refers to the situation in which the induction algorithm generates a classifier that perfectly fits the training data but has lost the capability of generalizing to instances not presented during training. In other words, instead of learning, the classifier just memorizes the training instances.

45. What is the data structure of data mining?

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46. What are different types of Hypothesis Testing?

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The various kinds of hypothesis testing are as per the following:

T-test: A T-test is utilized when the standard deviation is unknown and the sample size is nearly small.

Chi-Square Test for Independence: These tests are utilized to discover the significance of the association between all categorical variables in the population sample.

Analysis of Variance (ANOVA): This type of hypothesis testing is utilized to examine contrasts between the methods in different clusters. This test is utilized comparatively to a T-test but is utilized for multiple groups.

Welch’s T-test: This test is utilized to discover the test for equality of means between two testing sample tests.

47. What is the difference between variance and covariance?

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Variance	Covariance
Variance and Covariance are two mathematical terms that are frequently in the Statistics field. Variance fundamentally processes how separated numbers are according to the mean	Covariance refers to how two random/irregular factors will change together. This is essentially used to compute the correlation between variables.

48. What is a machine learning-based approach to data mining?

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This question is the high-level Data Mining Interview Questions asked in an Interview. Machine learning is basically utilized in data mining since it covers automatic programmed processing systems, and it depends on logical or binary tasks. . Machine learning for the most part follows the rule that would permit us to manage more general information types, incorporating cases and in these sorts and number of attributes may differ. Machine learning is one of the famous procedures utilized for data mining and in Artificial intelligence too.

49. Describe the Data integration issues model?

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50. Why should we use data warehousing and how can you extract data for analysis?

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• It is separate from the operational database.
• Integrates data from heterogeneous systems.
• Storage a huge amount of data, more historical than current data.
• Does not require data to be highly accurate

51. What is Visualization?

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Visualization is for the depiction of data and to gain intuition about the data being observed. It assists the analysts in selecting display formats, viewer perspectives, and data representation schema.

52. Give some data mining tools?

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• DBMiner
• GeoMiner
• Multimedia miner
• WeblogMiner

53. What are the most significant advantages of Data Mining?

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There are many advantages to Data Mining. Some of them are listed below:

Data Mining is used to polish the raw data and make us able to explore, identify, and understand the patterns hidden within the data. It automates finding predictive information in large databases, thereby helping to identify the previously hidden patterns promptly.

54. What are ‘Training set’ and ‘Test set’?

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Training set	Test set
In various areas of information science like machine learning, a set of data is used to discover the potentially predictive relationship known as ‘Training Set’	while the Test set is used to test the accuracy of the hypotheses generated by the learner, and it is the set of examples held back from the learner
The training set is an example given to the learner	The training set is distinct from the Test set.

55. Explain what is the function of ‘Unsupervised Learning?

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• Find clusters of the data
• Find low-dimensional representations of the data
• Find interesting directions in data
• Interesting coordinates and correlations
• Find novel observations/ database cleaning

56. In what areas Pattern Recognition is used?

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• Pattern Recognition can be used in:
• Computer Vision
• Speech Recognition
• Data Mining
• Statistics
• Informal Retrieval
• Bioinformatics

57. Explain the architecture of Oracle data miner?

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58. What is the general principle of an ensemble method and what is bagging and boosting in the ensemble method?

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The general principle of an ensemble method is to combine the predictions of several models built with a given learning algorithm to improve robustness over a single model. Bagging is a method in an ensemble for improving unstable estimation or classification schemes. While boosting methods are used sequentially to reduce the bias of the combined model. Boosting and Bagging both can reduce errors by reducing the variance term.

59. What are the components of relational evaluation techniques?

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The important components of relational evaluation techniques are:

• Data Acquisition
• Ground Truth Acquisition
• Cross-Validation Technique
• Query Type
• Scoring Metric
• Significance Test

60. What are the different methods for Sequential Supervised Learning?

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The different methods to solve Sequential Supervised Learning problems are:

• Sliding-window methods
• Recurrent sliding windows
• Hidden Markow models
• Maximum entropy Markov models
• Conditional random fields
• Graph transformer networks

61. Expalin the Data Warehouse Mining Architecture?

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62. What is reinforcement learning?

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Reinforcement Learning is a learning mechanism about how to map situations to actions. The end result should help you to increase the binary reward signal. In this method, a learner is not told which action to take but instead must discover which action offers a maximum reward. This method is based on the reward/penalty mechanism.

63. Is it possible to capture the correlation between continuous and categorical variables?

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Yes, we can use the analysis of the covariance technique to capture the association between continuous and categorical variables.

64. What is Visualization?

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Visualization is for the depiction of information and to acquire knowledge about the information being observed. It helps the experts in choosing format designs, viewer perspectives, and information representation patterns.

65. Name some best tools which can be used for data analysis.

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The most common useful tools for data analysis are:

• Google Search Operators

• KNIME
• Tableau
• Solver
• RapidMiner
• Io
• NodeXL

66. Describe the structure of Artificial Neural Networks?

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An artificial neural network (ANN) also referred to as simply a “Neural Network” (NN), could be a process model supported by biological neural networks. Its structure consists of an interconnected collection of artificial neurons

67. What are the cons of data mining?

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Security: The time at which users are online for various uses, must be important. They do not have security systems in place to protect us. As some of the data mining analytics use software. That is difficult to operate. Thus they require a user to have knowledge based training

68. What is Syntax for Task-Relevant Data Specification?

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• The Syntax of DMQL for specifying task-relevant data −
• use database database_name
• or
• use data warehouse data_warehouse_name
• in relevance to att_or_dim_list
• from relation(s)/cube(s) [where condition]
• order by order_list
• group by grouping_list

69. Is Google Analytics a data mining tool?

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Google Data Analytics tools are Data Analytical tools by Google Marketing Solutions. These tools help organizations gauge the success of their campaigns, determine user traffic sources, track the completion of multiple goals, and extract meaningful insights for intelligent decision-making.

70. What is Syntax for Specifying the Kind of Knowledge?

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Syntax for Characterization, Discrimination, Association, Classification, and Prediction.

71. Explain Syntax for Interestingness Measures Specification?

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Interestingness measures and thresholds can be specified by the user with the statement − with threshold = threshold_value

72. Explain Syntax for Pattern Presentation and Visualization Specification?

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Generally, we have a syntax, which allows users to specify the display of discovered patterns in one or more forms. display as result_form

73. Explain Data Mining Languages Standardization?

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This will serve the following purposes −

• Basically, it helps the systematic development of data mining solutions.
• Also, improves interoperability among multiple data mining systems and functions.
• Generally, it helps in promoting education and rapid learning.
• Also, promotes the use of data mining systems in industry and society.

74. Describe the multi-tiered architecture of data mining warehouse?

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75. What are the different stages of Data Mining?

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The three main stages are:

• Exploration
• Model Building and Validation
• Deployment

76. Define the Exploration Stage in Data Mining?

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The Exploration stage is mainly focused on collecting data from various sources and preparing it for later transformation and cleaning activities.

77. Define metadata?

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Metadata can simply be defined as data about data. Metadata is the summarized data that takes us to the detailed data

78. Why are the Model Building and Validation stage important in Data Mining?

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It is important since, in this stage, data is validated by using different models and is compared to finalize the model with the best performance.

79. In Data Mining, what are “Continuous” and “Discrete” data?

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Continuous	Discrete
Continuous data” is the data that changes continuously in a well-structured manner. The perfect example of this is age.	Discrete data” is when data is finite and has a specific meaning present in it. The most suitable example of this is gender.

80. In data mining, what are the required technological drivers?

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Query Complexity: In order to analyze a large number of complex queries, we require a very powerful system.

Database size: In order to process and maintain a huge collection of data, we require powerful systems.

81. What does ODS stand for?

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ODS stands for Operational Data Store

82. What is a Sting?

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Statistical Information Grid is called STING; it is a grid-based multi-resolution clustering strategy. In the STING strategy, every one of the items is contained into rectangular cells, these cells are kept into different degrees of resolutions and these levels are organized in a hierarchical structure.

83. semantic web mining?

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84. What are the important steps in the data validation process?

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As the name proposes Data Validation is the process of approving information. This progression principally has two methods associated with it. These are Data Screening and Data Verification.

Data Screening: Different kinds of calculations are utilized in this progression to screen the whole information to discover any inaccurate qualities.

Data Verification: Each and every presumed value is assessed on different use-cases, and afterward a final conclusion is taken on whether the value must be remembered for the information or not.

85. What is the K-means algorithm?

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K-means clustering algorithm – It is the simplest unsupervised learning algorithm that solves clustering problems. K-means algorithm partition n observations into k clusters where each observation belongs to the cluster with the nearest mean serving as a prototype of the cluster.

86. What is ensemble learning?

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To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined to solve a particular computational program Multiple. This process is known as ensemble learning. Ensemble learning is used when we build component classifiers that are more accurate and independent of each other. This learning is used to improve classification, prediction of data, and function approximation.

87. What is a Random Forest?

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Random forest is a machine learning method that helps you to perform all types of regression and classification tasks. It is also used for treating missing values and outlier values.

88. What is the Scope of Data Mining?

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It helps automate the process of analyzing and identifying predictive information in a huge amount of databases and datasets. Data Mining tools can help scrape and sweep through a diverse range of data in order to identify a pattern that was previously hidden.

89. What are the important steps in the data validation process?

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Data screening	Data verification
Different kinds of calculations are utilized in this progression to screen the whole information to discover any inaccurate qualities.	Each and every presumed value is assessed on different use-cases, and afterward a final conclusion is taken on whether the value must be remembered for the information or not.

90. Expalin the architecture of KDD process in data mining?

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91. What are the different types of machine learning?

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Machine Learning methods are divided into three categories.

Supervised Learning:

Machines learn under the supervision of labeled data in this sort of machine learning approach. The machine is trained on a training dataset, and it produces results by its training.

Unsupervised Learning:

Unsupervised learning contains unlabeled data, unlike supervised learning. As a result, there is no oversight over how it processes data. Unsupervised learning is to find patterns in data and group related items into clusters. When fresh input data is loaded into the model, the entity is no longer identified; instead, it is placed in a cluster of related objects.

Reinforcement Learning:

Models that learn and traverse to find the greatest feasible move are examples of reinforcement learning. Reinforcement learning algorithms are built in such a manner that they aim to identify the best feasible set of actions based on the reward and punishment principle.

92. What is the difference between deep learning and machine learning?

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Machine learning is a set of algorithms that learn from data patterns and then apply that knowledge to decision-making. Deep learning, on the other hand, can learn on its own by processing data, much as the human brain does when it recognizes something, analyzes it, and makes a conclusion. The main distinctions are the way data is provided to the system. Machine learning algorithms usually require structured input, whereas deep learning networks use layers of artificial neural networks.

93. In machine learning, what is a hypothesis?

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Machine learning helps you to use the data you have to better understand a certain function that best translates inputs to outputs. Function approximation is the term for this problem. You must use an estimate for the unknown target function that translates all the conceivable observations based on the provided situation in the best way possible. In machine learning, a hypothesis is a model that aids in estimating the target function and completing the required input-to-output mappings. You may specify the space of probable hypotheses that the model can represent by choosing and configuring algorithms.