30 Most Popular Data Mining Interview Questions Answers
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Through this Data Mining tutorial, you will get 30 Popular Data Mining Interview Questions Answers. As this blog contains Popular Data Mining Interview Questions Answers, which are frequently asked in data science interviews. Also, this Popular Interview Questions Answers on Data Mining contains answers to the questions to help you to crack the interview for the data scientist job.
2. 30 Top Data Mining Interview Questions Answers
Q.1. What are foundations of data mining?
Generally, we use it for a long process of research and product development. Also, we can say this evolution was started when business data was first stored on computers. We can also navigate through their data in real time. Data Mining is also popular in the business community. As this is supported by three technologies that are now mature: Massive data collection, Powerful multiprocessor computers, and Data mining algorithms.
Q.2. What is the scope of data mining?
- Automated prediction of trends and behaviours- We use to automate the process of finding predictive information in large databases. Also, questions that required extensive hands-on analysis can now be answered from the data. Moreover, targeted marketing is a typical example of predictive marketing. As we also use data mining on past promotional mailings.
- Automated discovery of previously unknown patterns – As we use data mining tools to sweep through databases. Also, to identify previously hidden patterns in one step. Basically, there is a very good example of pattern discovery. As it is the analysis of retail sales data. Moreover, that is to identify unrelated products that are often purchased together.
Q.3 What are advantages of data mining?
Basically, to find probable defaulters, we use data mining in banks and financial institutions. Also, this is done based on past transactions, user behaviour and data patterns.
Generally, it helps advertisers to push the right advertisements to the internet. Also, it surfer on web pages based on machine learning algorithms. Moreover, this way data mining benefit both possible buyers as well as sellers of the various products.
Basically, the retail malls and grocery stores peoples used it. Also, it is to arrange and keep most sellable items in the most attentive positions.
Q.4. What are the cons of data mining?
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. The techniques of data mining are not 100% accurate. Hence, it may cause serious consequences in certain conditions.
Read more about data mining Disadvantages
Q.5 Name Data mining techniques?
a. Classification Analysis
b. Association Rule Learning
c. Anomaly or Outlier Detection
d. Clustering Analysis
e. Regression Analysis
g. Sequential Patterns
h. Decision trees
Q.6. Give a brief introduction to data mining process?
Basically, data mining is the latest technology. Also, it is a process of discovering hidden valuable knowledge by analyzing a large amount of data. Moreover. we have to store that data in different databases. As data mining is a very important process. It becomes an advantage for various industries.
Read more about Data Mining Process
Q.7. Name types of data mining?
a. Data cleaning
d. Data transformation
e. Data mining
f. Pattern evaluation
g. Knowledge representation
Q.8. Name the steps used in data mining?
a. Business understanding
b. Data understanding
c. Data preparation
Q.9. Name areas of applications of data mining?
a. Data Mining Applications for Finance
i. Crime Agencies
j. Businesses Benefit from data mining
Q.10. What is required technological drivers in data mining?
Database size: Basically, as for maintaining and processing the huge amount of data, we need powerful systems.
Query Complexity: Generally, to analyze the complex and large number of queries, we need a more powerful system.
Data Mining Interview Questions Answers for Freshers – Q. 1,2,3,4,5,7,8,9
Data Mining Interview Questions Answers for Experience – Q. 6,10
Q.11. Give an introduction to data mining query language?
It was proposed by Han, Fu, Wang, et al. for the DBMiner data mining system. Although, it was based on the Structured Query Language. These query languages are designed to support ad hoc and interactive data mining. Also, it provides commands for specifying primitives. We can use DMQL to work with databases and data warehouses as well. We can also use it to define data mining tasks. Particularly we examine how to define data warehouses and data marts in DMQL.
Q.12. What is Syntax for Task-Relevant Data Specification?
The Syntax of DMQL for specifying task-relevant data −
use database database_name
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
Q.13. What is Syntax for Specifying the Kind of Knowledge?
Syntax for Characterization, Discrimination, Association, Classification, and Prediction.
Q.14. Explain Syntax for Interestingness Measures Specification?
Interestingness measures and thresholds can be specified by the user with the statement − with <interest_measure_name> threshold = threshold_value
Q.15. Explain Syntax for Pattern Presentation and Visualization Specification?
Generally, we have a syntax, which allows users to specify the display of discovered patterns in one or more forms. display as <result_form>
Q.16. Explain Data Mining Languages Standardization?
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.
Q.17. Explain useful data mining queries?
- First of all, it helps to apply the model to new data, to make single or multiple predictions.
- Also, you can provide input values as parameters, or in a batch.
- While it gets a statistical summary of the data used for training. Also, extract patterns and rule of the typical case representing a pattern in the model.
- Also, helps in extracting regression formulas and other calculations that explain patterns.
- Get the cases that fit a particular pattern.
- Further, it retrieves details about individual cases used in the model.
- Also, it includes data not used in the analysis. Moreover, it retrains a model by adding new data or perform cross-prediction.
Q.18. Give a brief introduction to data mining knowledge discovery?
Generally, most people don’t differentiate data mining from knowledge discovery. While others view data mining as an essential step in the process of knowledge discovery.
Q.19. Explain steps involved in data mining knowledge process?
Data Cleaning −
Basically, in this step, the noise and inconsistent data are removed.
Data Integration −
Moreover, in this step, multiple data sources are combined.
Data Selection −
Furthermore, in this step, data relevant to the analysis task are retrieved from the database.
Data Transformation −
Basically, in this step, data is transformed into forms appropriate for mining. Also, by performing summary or aggregation operations.
Data Mining −
In this, intelligent methods are applied in order to extract data patterns.
Pattern Evaluation −
While, in this step, data patterns are evaluated.
Knowledge Presentation −
Generally, in this step, knowledge is represented
Q.20. What are issues in data mining?
A number of issues that need to be addressed by any serious data mining package
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
Understandably of Discovered Knowledge: Consistency between Data and Discovered Knowledge.
Data Mining Interview Questions Answers for Freshers – Q. 11,16,17,18,19
Data Mining Interview Questions Answers for Experience – Q. 12,13,14,15,20
Q.21. What are major elements of data mining, explain?
Generally, helps in an extract, transform and load transaction data onto the data warehouse system.
While it stores and manages the data in a multidimensional database system.
Also, provide data access to business analysts and information technology professionals.
Generally, analyze the data by application software.
While, it shows the data in a useful format, such as a graph or table
Q.22. Name different level of analysis of data mining?
b. Genetic algorithms
c. Nearest neighbor method
d. Rule induction
e Data visualization
Q.23. Name methods of classification methods?
a. Statistical Procedure Based Approach
b Machine Learning Based Approach
d. Classification Algorithms
e. ID3 Algorithm
f. C4.5 Algorithm
g. K Nearest Neighbors Algorithm
H. Naïve Bayes Algorithm
i. SVM Algorithm
J. ANN Algorithm
K. 48 Decision Trees
l. Support Vector Machines
M. SenseClusters (an adaptation of the K-means clustering algorithm)
Q.24. Explain Statistical Procedure Based Approach?
Especially, there are two main phases present to work on classification. Also, it can be easily identified within the statistical community.
While, the second, “modern” phase concentrated on more flexible classes of models. Also, in which many of which attempt has to take. Moreover, it provides an estimate of the joint distribution of the feature within each class. Further, that can, in turn, provide a classification rule.
Generally, statistical procedures have to characterize by having a precise fundamental probability model and that is used to provides a probability of being in each class instead of just a classification.
Also, we can assume that the techniques will use by statisticians. Hence some human involvement has to assume with regard to variable selection.
Also, transformation and overall structuring of the problem.
Q.25. Explain Machine Learning Based Approach?
Generally, it covers automatic computing procedures. Also, it was based on logical or binary operations. Further, we use to learn a task from a series of examples.
Here, we have to focus on decision-tree approaches. Also, ss classification results come from a sequence of logical steps.
Also, its principle would allow us to deal with more general types of data including cases. While, the number and type of attributes may vary.
Q.26. Explain ID3 Algorithm?
Generally, the id3 calculation starts with the original set as the root hub. Also, on every cycle, it emphasizes through every unused attribute of the set and figures. Moreover, the entropy of attribute. Furthermore, at that point chooses the attribute. Also, it has the smallest entropy value.
Q.27. Name methods of clustering?
They are classified into the following categories −
- Partitioning Method
- Hierarchical Method
- Density-based Method
- Grid-Based Method
- Model-Based Method
- Constraint-based Method
Q.28. What do OLAP and OLTP stand for?
Basically, OLAP is an acronym for Online Analytical Processing and OLTP is an acronym for Online Transactional Processing.
Q.29. Define metadata?
Basically, metadata is simply defined as data about data. In other words, we can say that metadata is the summarized data that leads us to the detailed data.
Q.30. List the types of OLAP server?
Basically, there are four types of OLAP servers, namely Relational OLAP, Multidimensional OLAP, Hybrid OLAP, and Specialized SQL Servers.
Data Mining Interview Questions Answers for Freshers – Q. 21,22,23,27,28,29,30
Data Mining Interview Questions Answers for Experience – Q. 24,25,26
As a result, we have studied Popular Data Mining Interview Questions Answers. Also, I hope this Popular Data Mining Interview Questions Answers will help you to resolve your queries. In conclusion, I hope this blog will act as a gateway to your Data Mining Job. Furthermore, if you feel any query so, you can freely ask in comment box.
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