30 Frequently Asked Data Mining Interview Questions-Answers

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1. Objective

In this Data Mining blog, we will be going to provide you 30 frequently asked Data Mining Interview Questions-Answers being shared by industry experts. Also, Data Mining Interview Questions-Answers are totally based on Data Mining hot topics. Moreover, these frequently asked Data Mining Interview Questions-Answers will help you in cracking interviews.

30 Frequently Asked Data Mining Interview Questions-Answers

30 Frequently Asked Data Mining Interview Questions-Answers

2. Best Data Mining Interview Questions-Answers

Q.1. What is Fact Table?

It contains the measurement of business processes. Also, foreign keys for the dimension tables.

Q.2. What does subject-oriented data warehouse signify?

Basically, we use it to shows that the data warehouse stores the information around a particular subject. Such as product, customer, sales, etc.

Q.3. Explain is data mining?

As we know data mining is a set of methods. Also, in addition, we should apply it to a large and complex database.

Read more about the introduction to data mining

Q.4. What is a history of data mining?

First of all, in 1960s statisticians used the terms “Data Fishing” or “Data Dredging”. That was to refer what they considered the bad practice of analyzing data. Consequently, the term “Data Mining” appeared around 1990 in the database community.

Q.5. What are techniques used for data mining?

a. Artificial neural networks –

Generally, we use data mining in many ways. Just like in ANN it is used for non-linear predictive models. Also, it should be learning through training. Also, resemble biological neural networks in structure.

b. Decision trees-

Generally, Tree-shaped structures are used to represent sets of decisions. Also, for the classification of dataset rules are generated.

c. Genetic algorithms –

Basically, most of the genetic algorithms are present with the use of data mining. Also, these are a genetic combination, mutation, and natural selection for optimization techniques.

Read more about Data Mining Algorithms

Q.6. Why is Data Mining hot cake topic for this generation?

As we know that data mining is having spacious applications especially today. Hence, it is the young, hot and promising field for the present generation. Also, a good thing about this is that it has an attracted a great deal of attention in the information industry and in society.

Q.7. What are applications areas of data mining?

  • Weather forecasting.
  • E-commerce.
  • Self-driving cars.
  • Hazards of new medicine.
  • Space research.
  • Fraud detection.
  • Stock trade analysis.
  • Business forecasting.
  • Social networks.
  • Customers likelihood.

Q.8. Explain applications of data mining?

  • First of all, a credit card company use to leverage vast warehouse of customer transaction data. Also, we need to perform this to identify customers consequently.
  • There are too many analysis methods, we need to the manufacturer it for data mining. Then select promotional strategies that best reach their target customer segments.

Read more about detail data mining applications

Q.9. Explain areas where data mining has good effects?

  • Predict future trends, customer purchase habits
  • Help with decision making
  • Improve company revenue and lower costs
  • Market basket analysis

Q.10. Explain areas where data mining has bad effects?

  • User privacy/security
  • Amount of data is overwhelming
  • Great cost at implementation stage
  • Possible misuse of information
  • Possible inaccuracy of data

Read more about Disadvantages of Data Mining

Data Mining Interview Questions for Freshers- Q. 1,2,3,4,5,6,7,8

Data Mining Interview Questions for Experienced or Professionals- Q. 9,10

Q.11. What is classification?

It seems like these are the examples, where the data analysis task is Classification − A bank loan officer wants to analyze the data in order to know which customer is risky or which are safe. A marketing manager at a company needs to analyze a customer with a given profile, who will buy a new computer.

Read more about classification in detail

Q.12. Why is classification needed?

Most noteworthy, in today’s world of “big data”, a large database is becoming a norm. Just imagine there present a database with many terabytes. As facebook alone crunches 600 terabytes of new data every single day. Also, the primary challenge of big data is how to make sense of it. Moreover, the sheer volume is not the only problem. Also, big data need to be diverse, unstructured and fast changing. Consider audio and video data, social media posts, 3D data or geospatial data. Also, this kind of data is not easily categorized or organized. Further, to meet this challenge, a range of automatic methods for extracting information.

Q.13. What is Clustering?

Generally, a group of abstract objects into classes of similar objects is made. Although, we treat a cluster of data objects as one group. Also, while performing cluster analysis, we first partition the set of data into groups. As it was based on data similarity. Then we need to assign the labels to the groups. Moreover, a main advantage of over-classification is that it is adaptable to changes. Also, it helps single out useful features that distinguish different groups.

Read more about detail in clustering

Q.14. What is Cluster Analysis?

Basically, finding groups of objects such that the objects in a group will be like one another. Also, it’s different from the objects in other groups.

Q. 15. Explain the grid-based method?

Particularly, in a grid-based method, the objects together form a grid. Also, object space is quantized into a finite number of cells that form a grid structure.

Q.16. Explain the density-based method?

As it is based on the notion of density. The main idea behind this method is to continue growing the given cluster.

Q.17. Explain Model-based Method?

In this method, basically, a model is hypothesized for each cluster to find the best fit of data for a given model. Also, we use this method to locates the clusters by clustering the density function.

Q.18. Explain constraint-based method?

Basically, a constraint is referred to the user expectation. Also, it provides us with an interactive way of communication with the clustering process. Although, it can be specified by the user or the application need.

Q.19. Explain what is not cluster analysis?

Supervised classification – Have class label information

Simple segmentation – Dividing students into different registration groups, by the last name

Results of a query – Basically, groupings are a result of an external specification

Graph partitioning – Some mutual relevance and synergy, but areas are not identical

Q.20. Name some Data mining best books?

a. “ Introduction to data mining” by Tan, Steinbach & Kumar (2006)

b. An Introduction to Statistical Learning: with Applications in R

c. Data Science for Business: What you need to know about data mining and data-analytic thinking

d. Modeling With Data

e. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners

f. Data Mining: Practical Machine Learning Tools and Techniques

g. Probabilistic Programming & Bayesian Methods for Hackers

More about data mining books

Data Mining Interview Questions for Freshers- Q. 11,12,13,14,20

Data Mining Interview Questions for Experienced or Professionals- Q. 15,16,17,18,19

Q.21. How can you deal with multi-source problems?

Basically, there are many ways to deal with multi-source problems. Also, we need to identify all records that are similar. We also need to put them together into a single record. Although, a record must contain the necessary attributes having no redundancy.

Q.22. Hierarchical Clustering Algorithm is known to be?

The algorithm which uses to combines and divides groups are already existing. That helps to create a hierarchical structure showcasing the manner in which these groups are merged or divided.

Q.23. Give an explanation of collaborative filtering?

It is just simply an algorithm. As we use it useful for creating a recommendation system. Also, it totally depends upon the behavioral data of the user.

Q.24. What is OLAP?

OLAP (Online Analytical Processing):

In the multidimensional model, we need to organize data into multiple dimensions. Although, each dimension contains multiple levels of abstraction defined by concept hierarchies. Also, OLAP provides a user-friendly environment for interactive data analysis.

Q.25. What is “data mining Interface”?

For better feedback to a user during the construction of a query, data mining interface is used in GUI form. Furthermore the GUI of data mining query improves the quality of the query.

Q.26. What is Business Intelligence?

Business Intelligence is also known as D.S.S – Decision support system which refers to the technologies, application, and practices for the collection, integration, and analysis of the business-related information or data. In addition it even helps to see the data on the information itself.

Q.27. What is Dimension Table?

Basically, dimension table is a table which contains attributes of measurements stored in fact tables. Also, this table consists of hierarchies, categories, and logic that can be used to traverse in nodes.

Q.28. Explain tiers in the tight-coupling data mining architecture?

First of all, we can define data layer as a database. This layer is an interface for all data sources.

While we use data mining application layer to retrieve data from a database. Some transformation routine has to perform here.

Front-end layer provides the intuitive and friendly user interface for end-user.

Read more about data mining architecture

Q.29. What does subject-oriented data warehouse signify?

Basically, subject-oriented signifies that the data warehouse stores the information around a particular subject such as product, customer, sales, etc.

Q.30. Explain 48 Decision Trees?

Basically, a decision tree is a predictive machine-learning model. As it uses to decides the target value of a new sample. Also, the internal nodes of a decision tree denote the different attributes. Although, the branches between the nodes tell us the possible values.

Data Mining Interview Questions for Freshers- Q. 24,25,26,30

Data Mining Interview Questions for Experienced or Professionals- Q. 21,22,23,28,29

3. Conclusion

As a result, we have discussed each and every type of frequently asked Data Mining Interview Questions-Answers in this respective blog. Also, in conclusion, this will surely help you to prepare your self for data mining interview as well. Moreover, it contains every type of Data Mining Interview Questions-Answers which is great for the interview as well knowledge purpose also. Furthermore, if you have any query, feel free to ask in a comment section.

Related Topic – Data Mining IQ Part – 3 

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  1. Den says:

    Thanks for sharing your thoughts on learn about business.

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