Interview Question For Data Mining – Most Asked

1. Interview Question For Data Mining

As we know Data Mining is a booming technology nowadays. Hence it is very important to know each and every aspect of Data Mining as well as Interview Question For Data Mining. So, this blog will definitely help you regarding Interview Question For Data Mining. In this blog, we will cover each and every aspect of Data Mining, which may also be possible frequently asked Interview Question For Data Mining. Moreover, we will try our best to provide each Question, that from now onwards your search for best and all Interview Questions For Data Mining will end here.

Interview Question For Data Mining

2. Top 35 Interview Question For Data Mining

Q.1. Explain the types of data mining?

a. Data Cleaning

In this process data gets cleaned. As we know data in the real world is noisy, inconsistent and incomplete. It includes a number of techniques. Such as filling in the missing values, combined compute. The output of data cleaning process is adequately cleaned data.

b. Data Integration

In this process data in integrated from different data sources into one. As data lies in different formats in a different location. We can store data in a database, text files, spreadsheets, documents, data cubes, and so on. Although, we can say data integration is so complex, tricky and difficult task. That is because normally data doesn’t match the different sources. We use metadata to reduce errors in the data integration process. Another issue faced is data redundancy.

In this case, same data might be available in different tables in the same database. Data integration tries to reduce redundancy to the maximum possible level. As without affecting the reliability of data.

c. Data Selection

This is the process by which data relevant to the analysis is retrieved from the database. As this process requires large volumes of historical data for analysis. So, usually, the data repository with integrated data contains much more data than actually required. From the available data, data of interest needs to be selected and stored.

Read more about data mining introduction

Q.2. Explain how data mining performed in detail?

a. Business understanding

First, we have to understand the requirements. Then have to find what are the business requirements. Next, the current situation has to access by finding out the different resources, assumptions. Also, by considering other important factors. Then, to achieve the business objectives we need to create data mining. Finally, we have to establish a new data mining plan to achieve both business and data mining goals. The plan should be as detailed as possible.

b. Data understanding

First, this phase starts with the collection of initial data. As in this, we have to collect data from available sources. As we have to collect data to get familiar with the data. Also, in order to make data collection, we need some activities that need to be performed. Such as data load and data integration. Next, the “gross” or “surface” properties of acquired data need to be examined and reported.

Then, we need to explore the data needs by tackling the data mining question. That can be addressed using querying, reporting, and visualization. Finally, have to examine the data quality by answering some important question. Such as “Is the acquired data complete?”, “Is there any missing values in the acquired data?”

c. Data preparation

In this data, preparation process our 90% time consumed in our project. Also, it’s outcome is the final data set. Once we identify the data sources, then we need to select, clean, construct and have to format in the desired form. The data exploration task has to do with a greater depth. That need to be carry during this phase to notice the patterns. That is based on business understanding.

d. Modeling

First, we have to select modeling techniques that we need to use for the prepared dataset. Next, we have to generate test scenario to validate the quality and validity of the model. Then, by using modeling tools we have to prepare one or more models on the dataset. Finally, by involving these models need to be assessed involving stakeholders. That is to make sure that created models are met business initiatives.

e. Evaluation

Particularly, in this case, have to evaluate the result in the context of the business goal. In this phase, due to new patterns, new business requirements occurs. That patterns have to discover in the model results or from other factors. Gaining business understanding is an iterative process in data mining. The go or no-go decision must be made in this step to move to the deployment phase.

f. Deployment

The information, which we gain through data mining process, we need to present it. The information has to represent in such a way that stakeholders can use it whenever they want it. Based on the business requirements, the deployment phase could be creating a report.
Also, as complex as a repeatable data mining process across the organization. In this plans for deployment, maintenance, have to be created for implementation. and also future supports. From the project point, the final report needs to summary the project experiences. And, review the project to see what need to improved created learned lessons. The CRISP-DM offers a uniform framework for experience documentation and guidelines. In addition, the CRISP-DM can apply in various industries with different types of data.

Read more about Data Mining Process

Q.3. Name some terminologies of data mining?

a. Cleaning (cleansing)

It is a process of preparing data for a data mining activity. Obvious data errors are detected and corrected and missing data is replaced.

b. Confusion matrix

We use this matrix shows that counts of the actual versus predicted class values. It shows not only how well the model predicts. But also presents the details needed to see exactly where things may have gone wrong. Consequent When an association between two variables is defined, the second item is called the consequent.

c. Continuous

Continuous data can have any value in an interval of real numbers. That is, the value does not have to be an integer. Continuous is the opposite of discrete or categorical.nbsp; Cross-validation A method of estimating the accuracy of a classification or regression model.
Read more about Data Mining Terminologies

Q.4. Explain applications of data mining?

a. For Finance

We have to Increase customer loyalty by collecting and analyzing customer behavior data. Also, one needs to help banks. That predict customer behavior and launch relevant services and products. Helps in Discovering hidden correlations between various financial indicators. That need to detect suspicious activities with a high potential risk. Generally, it identifies fraudulent or non-fraudulent actions. As it done by collecting historical data. And then turning it into valid and useful information.

b. Data mining applications for Healthcare

Basically, it provides government, regulatory and competitor information that can fuel competitive advantage. Although, it supports the R&D process. And then go-to-market strategy with rapid access to information at every phase. Generally, it discovers the relationships between diseases and the effectiveness of treatments. That is to identify new drugs or to ensure that patients receive appropriate, timely care. Also, it supports healthcare insurers in detecting fraud and abuse.

c. Data mining applications for Intelligence

Generally, it reveals hidden data related to money laundering, narcotics trafficking, etc. Also, helps in Improving intrusion detection with a high focus on anomaly detection. And identify suspicious activity from a day one. Basically, convert text-based crime reports into word processing files. That can be used to support the crime-matching process.
Read more about data mining application

Q.5. What are pros of data mining?

a. Marketing / Retail Marketing companies use data mining to build models. That was based on historical data to predict who will respond to the new marketing campaigns. Such as direct mail, online marketing campaign etc. As a result, marketers have an approach to selling profitable products to targeted customers.

b. Finance / Banking As data mining provides financial institutions information about loan information and credit reporting. By building a model from historical customer’s data can determine good and bad loans. Besides, it helps banks detect fraudulent credit card transactions. That is to protect credit card’s owner.

c. Governments We use data mining in government agencies. That is by digging and analyzing records of the financial transaction. That is to build patterns that can detect money laundering.

d. Banking/Crediting As data mining is also used in financial institutions in areas. Such as credit reporting and loan information.

e. Law enforcement We use data mining in law enforcers to identify criminal suspects. Also, apprehending these criminals by examining trends in location. And also in other patterns of behaviors.

Read more about Advantages of Data Mining

Q.6. Explain data mining techniques?

a. Decision Trees

It’s the most common technique, we use for data mining. As because of its simplest structure. The root of decision tree act as a condition. Each answer leads to specific data that help us to determine final decision based upon it.

b. Sequential Patterns

As we use this to discover regular events, similar patterns in transaction data. The historical data of customers helps us to identify the past transactions in a year. Clustering: Having similar characteristics clusters objects have to form, by using automatic method. We use clustering, to define classes. Then suitable objects have to place in each class.

c. Prediction

We use this method defines the relationship between independent and dependent instances.

d. Association

It is also known as relation technique. Also, in this, we have to recognize a pattern. That it is based upon the relationship of items in a single transaction. Also, we can suggest the technique for market basket analysis. That is to explore the products that customer frequently demands.

Read more about Data Mining Techniques

Q.7. Explain data mining architecture?

Data mining system contains too many components. That is a data source, data warehouse server, data mining engine, and knowledge base.

a) Data Sources

There are so many documents present. That is a database, data warehouse, World Wide Web (WWW). That are the actual sources of data. Sometimes, data may reside even in plain text files or spreadsheets. World Wide Web or the Internet is another big source of data.

b) Database or Data Warehouse Server

The database server contains the actual data that is ready to be processed. Hence, the server handles retrieving the relevant data. That is based on the data mining request of the user.

c) Data Mining Engine

In data mining system data mining engine is the core component. It consists of a number of modules. That we used to perform data mining tasks. That includes association, classification, characterization, clustering, prediction, etc.

d) Pattern Evaluation Modules

This module is mainly responsible for the measure of interestingness of the pattern. For this, we use a threshold value. Also, it interacts with the data mining engine. That’s the main focus is to search towards interesting patterns.

e) Graphical User Interface

We use this interface to communicate between the user and the data mining system. Also, this module helps the user use the system easily and efficiently. They don’t know the real complexity of the process. When the user specifies a query, this module interacts with the data mining system. Thus, displays the result in an easily understandable manner.

f) Knowledge Base

In whole data mining process, the knowledge base is beneficial. We use it to guiding the search for the result patterns. The knowledge base might even contain user beliefs and data from user experiences. That can be useful in the process of data mining. The data mining engine might get inputs from the knowledge. That is the base to make the result more accurate and reliable. The pattern evaluation module interacts with the knowledge base. That is on a regular basis to get inputs and also to update it.

Read more about data mining architecture in details

For Freshers – Interview Question for Data Mining. Q- 1,3,4,5,6

For Experienced – Interview Question for Data Mining. Q- 2,7

Q.8. Explain in detail Syntax for Specifying the Kind of Knowledge?

a. Characterization

The syntax for characterization is −
mine characteristics [as pattern_name]
analyze {measure(s) }
The analyze clause, specifies aggregate measures, such as count, sum, or count%

b. Discrimination

The syntax for Discrimination is
− mine comparison [as {pattern_name]}
For {target_class } where {t arget_condition }
{versus {contrast_class_i }
where {contrast_condition_i}}
analyze {measure(s) }

c. Association

The syntax for Association is−
mine associations [ as {pattern_name} ]
{matching {metapattern} }

d. Classification

The syntax for Classification is −
mine classification [as pattern_name]
analyze classifying_attribute_or_dimension

e. Prediction

The syntax for prediction is −
mine prediction [as pattern_name]
analyze prediction_attribute_or_dimension
{set {attribute_or_dimension_i= value_i}}

Q.9. What are aspects of data mining?

a. Data Integration: First of all the data is collected and integrated from all the different sources.

b. Data Selection: Generally, we may not all the data we have collected in the first step. Also, in this step, we select only those data which we think useful for data mining.

c. Data Cleaning: Generally, the data we have collected is not clean. And may contain errors, missing values, noisy or inconsistent data. Therefore we need to apply different techniques to get rid of such anomalies.

d. Data Transformation: Basically, the data even after cleaning is not ready for mining. Also, we need to transform them into forms appropriate for mining. Thus, the techniques used to do this are smoothing, aggregation, normalization etc.

e. Data Mining: As now in this step, we are ready to apply data mining techniques on the data. Basically, it is to discover the interesting patterns. Hence, clustering and association analysis are among the many different techniques present. Also, as we used for data mining.

f. Pattern Evaluation and Knowledge Presentation: Generally, this step includes visualization, transformation, removing redundant patterns from the patterns we generated.

g. Decisions / Use of Discovered Knowledge: As this step is beneficial to us. Also, it helps to use the knowledge acquired to take better decisions.

Q.10. Explain in detail different level of data analysis?

a. Artificial Neural Networks: Basically, there are present non-linear predictive models. That learn through training and resemble biological neural networks in structure.

b. Genetic algorithms: Generally, there are optimization techniques that use this process. Such as genetic combination, mutation. Also, natural selection in a design based on the concepts of natural evolution.

c. Nearest neighbor method: Basically, it is a technique which classifies each record in a dataset. Also, as it is based on a combination of the classes of the k record(s) most similar to it in a historical data set.

d. Rule induction: Generally, the extraction of useful if-then rules from data based on statistical significance.

e. Data visualization: Basically, the visual interpretation of complex relationships in multidimensional data. Also, we use graphics tools to illustrate data relationships.

Q.11. Explain syntax for soncept hierarchy specification?

We use the following syntax to specify concept hierarchies−
use hierarchy <hierarchy> for <attribute_or_dimension>

We use different syntaxes to define different types of hierarchies such as−
-schema hierarchies
define hierarchy time_hierarchy on date as [date,month quarter,year] –
set-grouping hierarchies
define hierarchy age_hierarchy for age on customer as
level1: {young, middle_aged, senior} < level0: all
level2: {20, …, 39} < level1: young
level3: {40, …, 59} < level1: middle_aged
level4: {60, …, 89} < level1: senior
-operation-derived hierarchies
define hierarchy age_hierarchy for age on customer
as {age_category(1), …, age_category(5)}
:= cluster(default, age, 5)< all(age)
-rule-based hierarchies
define hierarchy profit_margin_hierarchy on item as
level_1: low_profit_margin < level_0: all
if (price – cost)< $50
level_1: medium-profit_margin < level_0: all
if ((price – cost) > $50) and ((price – cost) ≤ $250))
level_1: high_profit_margin < level_0: all

Q.12. What is business intelligence?

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

Q.13. Give an explanation of collaborative filtering.

Collaborative filtering can be said to be a simple algorithm used for creating a recommendation system that depends on the behavioral data of the user.

Q.14. Explain how to work with the data mining algorithms included in SQL server data mining?

SQL Server data mining offers Data Mining Add-ins for office 2007 that allows discovering the patterns and relationships of the data. This also helps in an enhanced analysis. The Add-in called Data Mining Client for Excel is used to first prepare data, build, evaluate, manage and predict results.

For Freshers – Interview Question for Data Mining. Q-9,10,12,13,14

For Experienced – Interview Question for Data Mining. Q-8,11

Q.15. Explain the concepts and capabilities of data mining?
Data mining is used to examine or explore the data using queries. These queries can be fired on the data warehouse. Explore the data in data mining helps in reporting, planning strategies, finding meaningful patterns etc. it is more commonly used to transform a large amount of data into a meaningful form. Data here can be facts, numbers or any real-time information like sales figures, cost, metadata etc. The information would be the patterns and the relationships amongst the data that can provide information.

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Q.16. How do the data mining and data warehousing work together?

Data warehousing can be used for analyzing the business needs by storing data in a meaningful form. Using Data mining, one can forecast the business needs. A data warehouse can act as a source of this forecasting.

Q.17. What is model in data mining World?

Models in Data mining help the different algorithms in decision making or pattern matching. The second stage of data mining involves considering various models and choosing the best one based on their predictive performance.

Q.18. What is discrete and continuous data in data mining world?

Discrete data can be considered as defined or finite data. E.g. Mobile numbers, gender. Continuous data can be considered as data which changes continuously and in an ordered fashion. E.g. age.

Q.19. What are the different problems that “data Mining” can solve?

Data mining helps analysts in making faster business decisions which increases revenue with lower costs.

Data mining helps to understand, explore and identify patterns of data.
We use data mining to automate the process of finding predictive information in large databases.
Also, helps to identify previously hidden patterns.

Q.20. What is data purging?

The process of cleaning junk data is termed as data purging. Purging data would mean getting rid of unnecessary NULL values of columns. This usually happens when the size of the database gets too large.

Q.21. 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.

Read more about Cluster analysis

For Freshers – Interview Question for Data Mining. Q-15,16,17,18

For Experienced – Interview Question for Data Mining. Q-19,20,21

Q.22. Explain partitioning method?

Partitioning Method Suppose we are given a database of ‘n’ objects. And the partitioning method constructs ‘k’ partition of data. Each partition will represent a cluster and k ≤ n. It means that it will classify the data into k groups. That must need to satisfy the following requirements − Each group contains at least one object. Each object must belong to exactly one group.
Points to remember − If we have a given number of partitions (say k). Then the partitioning method will create an initial partitioning. Further, it uses the iterative relocation technique. That is to improve the partitioning by moving objects from one group to other.

Q.23. What are requirements of clustering in data mining?

a. Scalability We need highly scalable clustering algorithms to deal with large databases.

b. Ability to deal with different kinds of attributes Algorithms should be capable to be applied to any kind of data. Such as interval-based data, categorical, and binary data.

c. Discovery of clusters with attribute shape The clustering algorithm should be capable of detecting clusters of arbitrary shape. They should not be bounded to only distance measures. That tend to find a spherical cluster of small sizes.

d. High dimensionality The clustering algorithm should not only be able to handle low-dimensional data. Although, need to handle the high dimensional space.

e. Ability to deal with noisy data Databases contain noisy, missing or erroneous data. Some algorithms are sensitive to such data and may lead to poor quality clusters.

f. Interpretability The clustering results should be interpretable, comprehensible, and usable.

Q.24. What are applications of cluster analysis?

We use clustering analysis in different applications. Such as market research, pattern recognition, data analysis, and image processing.
Clustering can also help marketers discover distinct groups in their customer base. Moreover, they can characterize their customer groups based on the purchasing patterns.
Basically, in the field of biology, it can be used to derive plant and animal taxonomies. categorize genes with similar functionalities and gain insight into structures inherent to populations.
Clustering also helps in identification of areas. That are of similar land use in an earth observation database. It also helps in the identification of groups of houses in a city. That is according to house type, value, and geographic location.
Clustering also helps in classifying documents on the web for information discovery Also, we use clustering in outlier detection applications. Such as detection of credit card fraud. As a data mining function, cluster analysis serves as a tool. That is to gain insight into the distribution of data. Also, need to observe characteristics of each cluster.

Q.25. Explain M. SenseClusters (an adaptation of the K-means clustering algorithm)

We have made use of SenseClusters to classify the email messages. That is into different user-define folders. SenseCluster available package of Perl programs. As it was developed at the University of Minnesota Duluth. That we use for automatic text and document classification. The advantage of SenseClusters is that it does not need any training data; It makes use of unsupervised learning methods to classify the available data. Now, particularly in this section will understand the K-means clustering algorithm. That has been use in SenseClusters. Clustering is the process in which we divide the available data. That instances into a given number of sub-groups. These sub-groups are clusters, and hence the name “Clustering”.

Q.26. Explain support vector machines?

Support Vector Machines are supervised learning methods. That used for classification, as well as regression. The advantage of this is that they can make use of certain kernels to transform the problem. Such that we can apply linear classification techniques to non-linear data. Applying the kernel equations. That arranges the data instances in a way within the multi-dimensional space. That there is a hyperplane that separates data instances of one kind from those of another. The kernel equations may be any function. That transforms the non-separable data in one domain into another domain. In which the instances become separable. Kernel equations may be linear, quadratic, Gaussian, or anything else. That achieves this particular purpose.

Q.27. Explain ANN algorithm?

Artificial neural networks are types of computer architecture inspired by biological neural networks. They are used to approximate functions. That can depend on a large number of inputs and are generally unknown. They are presented as systems of interconnected “neurons”. That can compute values from inputs. Also, they are capable of machine learning as well as pattern recognition. Due to their adaptive nature. An artificial neural network operates by creating connections between many different processing elements. That each corresponding to a single neuron in a biological brain. These neurons may be actually constructed or simulated by a digital computer system. Each neuron takes many input signal. That produces a single output signal that is sent as input to another neuron.

Read more about ANN Algorithm 

Q.28. Explain S.V.M algorithm?

SVM has attracted a great deal of attention in the last decade. It also applied to various domains applications. SVMs are used for learning classification, regression or ranking function. SVM is based on statistical learning theory and structural risk minimization principle. And have the aim of determining the location of decision boundaries. We call it as a hyperplane. That produces the optimal separation of classes. Thereby creating the largest possible distance between the separating hyperplane. Further, the instance need to proof. That is to reduce an upper bound on the expected generalization error.
Read more about SVM Algorithm

For Freshers – Interview Question for Data Mining. Q-22,27,28

For Experienced – Interview Question for Data Mining. Q-23,24,25,26

Q.29. Explain Naïve Bayes algorithm?

The Naive Bayes Classifier technique is based on Bayesian theorem. Particularly, we use it when the dimensionality of the inputs is high. The Bayesian Classifier is capable of calculating the possible output. It is also possible to add new raw data at runtime and have a better probabilistic classifier. This classifiers considers the presence of a particular feature of a class. That is unrelated to the presence of any other feature when the class variable is given.

Q.30. Explain K Nearest Neighbors algorithm?

The closest neighbor rule distinguishes the classification of an unknown data point. That is on the basis of its closest neighbor whose class is already known. M. Cover and P. E. Hart purpose k nearest neighbor (KNN). In which nearest neighbor is computed on the basis of estimation of k. That indicates how many nearest neighbors are to be considered to characterize. It makes use of the more than one closest neighbor to determine the class. In which the given data point belongs to and so it is called as KNN. These data samples are needed to be in the memory at the run time.

Read more about Machine Learning Algorithms

Q.31. Explain neural network?

The field of Neural Networks has arisen from diverse sources. That is ranging from understanding and emulating the human brain to broader issues. That is of copying human abilities such as speech and can be used in various fields. Such as banking, in classification program to categorize data as intrusive or normal.
Generally, neural networks consist of layers of interconnected nodes. That each node producing a non-linear function of its input. And input to a node may come from other nodes or directly from the input data. Also, need to identify some nodes with the output of the network. On the basis of this, there are different applications for neural networks present. That involve recognizing patterns and making simple decisions about them.

Read More about Neural Network

Q.32. Explain classification algorithms?

It is one of the Data Mining techniques. We use it to analyze a given data set and takes each instance of it. It assigns this instance to a particular class. Such that classification error will be least. Hence, we use this to extract models. That define important data classes within the given data set. Classification is a two-step process. During the first step, the model is created by applying classification algorithm. That is on training data set. Then in the second step, the extracted model is tested against a predefined test data set. That is to measure the model trained performance and accuracy. So classification is the process to assign class label from a data set whose class label is unknown.

Read more data mining interview questions and answers

Q.33. Explain tiers in the tight-coupling data mining architecture?

Data layer: We can define data layer as a database or data warehouse systems. This layer is an interface for all data sources. We store data mining results in the data layer. Thus, we can present to end-user in form of reports or another kind of visualization. We use data mining application layer is to retrieve data from a database. Some transformation routine have to perform here. That is to transform data into the desired format. Then we have to process data using various data mining algorithms. Front-end layer provides the intuitive and friendly user interface for end-user. Further,  to interact with data mining system. Data mining result presented in visualization form to the user in the front-end layer.

Q.34. What are the required technological drivers in data mining?

Basically, data mining applications are present for all size machines. Such as mainframe, workstations, clouds, client, and server. Further, the size of enterprise applications varies from 10 Gb to 100 Tb. To deliver the applications exceeding 100 Tb, we prefer NCR systems. The technological drivers are as: Database size: As for maintaining and processing the huge amount of data, we need powerful systems. Query Complexity: To analyze the complex and large number of queries, we need more powerful system

Q.35. How do we categorize data mining systems?

As there are too many data mining systems available. Also, some systems are specific. That we need to dedicate to a given data source. Further, according to various criteria, data mining systems have to categorize.

a. Classification according to the type of data source mined According to the type of data handle, have to perform classification of data mining. Such as spatial data, multimedia data, time-series data, text data, World Wide Web, etc.

b. Classification according to the data model drawn on
Generally, this classification is done on the basis of a data model. Such as relational database, object-oriented database, data warehouse, transactional, etc.

c. Classification according to the king of knowledge discovered
In this classification we perform this on the basis of the kind of knowledge. Such as characterization, discrimination, association, classification, clustering, etc.

d. Classification according to mining techniques used
As data mining systems employ are use to provide different techniques. According to the data analysis, we have to do this classification. Such as machine learning, neural networks, genetic algorithms, , etc.

For Freshers – Interview Question for Data Mining. Q-29,30,31,32

For Experienced – Interview Question for Data Mining. Q-33,34,35

3. Conclusion

Hence, we have tried to cover, all the possible frequent asked Interview Question For Data Mining which may ask in Data Mining Interview when you search for Data Mining jobs. However, if you want to add any question in Interview Question For Data Mining or if you want to ask any Query regarding interview Question For Data Mining, feel free to ask in the comment section. Moreover, we assure you that, we will definitely get back to you.