Most Asked Data Mining Interview Questions
1) What is Data Mining? / What do you understand by Data Mining?
Data Mining is a process of extracting usable data from a more extensive set of raw data by using some methods along with machine learning, statistics, and database systems. It implies analyzing data patterns in large batches of data using one or more software. Data mining is a specific subfield of Computer Science and Statistics. The main goal of Data Mining is to extract information (using intelligent methods) from a data set and transform the information into an understandable structure for further use.
Using Data Mining, businesses can learn more about their customers and develop more effective strategies to expand their various business functions and utilize their resources more optimally and insightfully. Data mining consists of useful data collection and warehousing as well as computer processing. It makes businesses to attain their objective and makes better decisions.
2) What are the key features of Data Mining?
Data mining has many applications in multiple fields, like science and research. Following is the list of key features of Data Mining:
3) What are the different fields where data mining is used?
Data Mining is mainly used by big consumer-based companies that focus on retail, financial, communication, and marketing fields. It is used to get the consumer's transactional data pattern to determine price, customer preferences, and product positioning, which later impact sales, customer satisfaction, and corporate profits.
Following is the list of most important areas where data mining is widely used:
Healthcare and Personal Grooming
Data mining has a significant impact in the field of healthcare. It uses data and analytics to identify the best practices that can improve care and reduce costs. Scientists use several Data Mining approaches like multi-dimensional databases, machine learning, soft computing, data visualization, statistics, etc., to make things easy for patients. Using Data Mining, we can predict the volume of patients in every category and make sure that the patients get the appropriate care at the right place and at the right time.
Market Basket Analysis
This modeling technique follows the theory that if you buy a specific group of items, you are more likely to buy another group of items. Using this technique, the retailer can understand the purchase behavior of a buyer and change the store's layout according to the buyer's needs.
Education & Training
Educational Data Mining is used to identify and predict the students' future learning behavior. If a student is studying a particular course, then the institutes can know which related course they may apply later by using Data Mining. This is also beneficial to make focus on what to teach and how to teach. The institutes can capture the learning pattern of the students and use to develop techniques to teach them.
By using Data mining tools, we can discover patterns in complex manufacturing processes. We can use this to predict the product development span time, cost, and dependencies, among other tasks.
Data Mining can be used as a perfect fraud detection system to protect the information of all users. By Data Mining, we can classify fraudulent or non-fraudulent data and make an algorithm to identify whether the record is fraudulent or not.
Customer Relationship Management
We can use Data Mining to maintain a proper relationship with a customer.
Some other areas where data mining is used:
4) What is the difference between Data Mining and Data Warehousing?
Data Warehousing mainly focuses on extracting data from different sources, cleaning the data, and storing it in the warehouses. On the other hand, Data Mining is used to study and explore the data using queries. In this process, the meaning pattern or data is extracted. We can also fire these queries on the data warehouses. After Data Mining, the explored information is used to report, plan strategies, find meaningful patterns, etc.
Example: A company's data warehouse stores all the relevant information of projects and employees. We can apply Data Mining queries to this data warehouse to get useful records.
5) What are the different types of Data Mining?
We can classify Data Mining into the following types:
6) What are the different techniques used for Data Mining?
Following is the list of most important Data Mining techniques:
Prediction: This technique specifies the relationship between independent and dependent instances. For example, while considering sales data, if we want to predict the future profit, the sale acts as a separate instance, whereas the payoff is the dependent instance. Accordingly, based on sales and profit's historical data, the associated profit is the predicted value.
Decision trees: It specifies a tree structure where the decision tree's root acts as a condition/question having multiple answers. Each answer sets to specific data that helps in determining the final decision based on the data.
Clustering analysis: This technique specifies that a cluster of objects having similar characteristics is formed automatically. The clustering method defines classes and then places suitable objects in each class.
Sequential Patterns: This technique is used to specify the pattern analysis used for discovering identical patterns in transaction data or regular events. For example, customers' historical data helps a brand identify the patterns in the transactions that happened in the past year.
Classification Analysis: This is a Machine Learning based method in which each item in a particular set is classified into predefined groups. It uses advanced techniques like linear programming, neural networks, decision trees, etc.
Association rule learning: This technique is used to create a pattern based on the items' relationship in a single transaction.
7) What do you understand by Data Purging?
Data Purging is a process that is used in database management systems to maintain relevant data in a database. It is used to clean the junk data by eliminating or deleting the row and columns' unnecessary NULL values. It is essential because whenever we need to load new data in the database, we have to purge the irrelevant data from the database.
Using Data Purging of the database frequently, we can remove the junk data that takes up a fair amount of database memory and slow down the database's performance. So, we can say that data purging is mandatory when the database's size gets too large.
8) What are cubes in Data Mining?
In Data Mining, cubes or data cubes are used to store data in a summarized version to analyze this faster when required. The data is stored in such a way that reporting becomes very easy.
For example, Organizations use data cubes to analyze the weekly or monthly performance of their employees. Here, month and week are considered as the dimensions of the cube.
9) What is the difference between OLAP and OLTP?
The terms OLAP and OLTP look similar but refer to different kinds of systems. We can divide an IT system into two categories: Analytical Process and Transactional Process.
10) What are the different storage models available in OLAP?
There are mainly three storage models available in OLAP. They are:
There are some advantages and disadvantages of using the above storage models.
11) What are the advantages and disadvantages of using the MOLAP storage model?
The term MOLAP stands for "Multidimensional Online Analytical Processing." As the name shows, it is a multidimensional storage model. This storage model type stores the data in multidimensional cubes and not in the standard relational databases.
Advantages of using the MOLAP storage model:
Disadvantages of using the MOLAP storage model:
12) What are the advantages and disadvantages of using the ROLAP storage model?
The term ROLAP stands for "Relational Online Analytical Processing." In this storage model, the data is stored in the form of a relational database.
Advantages of using the ROLAP storage model:
Disadvantages of using the ROLAP storage model:
13) What are the advantages and disadvantages of using the HOLAP storage model?
The term HOLAP stands for "Hybrid Online Analytical Processing." It is a combination of MOLAP and ROLAP. This is a hybrid storage model and was built to overcome the MOLAP and ROLAP storage model's limitations.
Advantages of using the HOLAP storage model:
Disadvantages of using HOLAP storage model:
14) What are the different problems that "Data Mining" can solve?
Data Mining can solve the following types of problems:
15) What is Discrete and Continuous data in Data Mining?
In Data Mining, discreet data is a type of data defined as finite data. This type of information is never changed.
Example: Mobile numbers, gender, etc. are the example of discreet data.
On the other hand, continuous data is a type of data that changes continuously and in an ordered fashion.
Example: Age is an example of continuous data.
16) What do you understand by a model in Data Mining?
In Data Mining, models help the different algorithms in decision making or pattern matching. In the second stage of Data Mining, we consider various models and choose the best one according to their predictive performance.
17) How do Data Mining and Data Warehousing work together?
Generally, Data Mining and Data Warehousing work together. Data Warehousing is used to analyze the business needs by storing data in a meaningful form, and Data Mining is used to forecast the business needs. So, here Data Warehouse can act as a source of this forecasting.
18) What are the different stages used in "Data Mining"?
Following are the three different stages used in Data Mining:
19) What is a Model in the field of Data Mining?
Model is an essential factor in Data Mining activities. It is used to define algorithms that help in decisions making and pattern matching.
20) What is the Naive Bayes Algorithm in Data Mining?
The Naive Bayes Algorithm is widely used in Data Mining to generate mining models. After that, these generated models are generally used to identify the relationship between the input columns and the predicated available columns. This algorithm is mainly used during the initial stages of the explorations.
21) What is Clustering Algorithm in Data Mining?
In Data Mining, the clustering algorithm is used to group sets of data with similar characteristics (also known as clusters). By the use of these clusters, we can make faster decisions and explore data. First, this algorithm identifies the relationships in a dataset, and then it generates a series of clusters based on the relationships. The process of creating clusters is also repetitive.
22) Which are the most popular areas of applications of Data Mining?
Following is the list of the most popular area of application of Data Mining Applications for Finance.
23) Explain the time series algorithm in Data Mining?
In Data Mining, the time series algorithm is mainly used for that type of data where the values are changed continuously based on time. For example, age.
This algorithm is used to predict the data set and then keep track of the continuous data and successfully choose the correct data. It also generates a specific model to predict the data's future trends based on the entire original data sets.
24) What do you understand by DMX in the context of Data Mining?
DMX is an acronym that stands for Data Mining Extensions. It is a query language for Data Mining models supported by Microsoft's SQL Server Analysis Services product. Same as SQL also supports a data definition language, data manipulation language, and a data query language, all three with SQL-like syntax.
25) What are the different functions of Data Mining?
Following is the list of different functions of Data Mining:
26) What do you understand by data aggregation and data generalization?
Data Aggregation: Data aggregation is a process where data is aggregated altogether, and we can construct a cube for data analysis purposes.
Data generalization: Data generalization is a process where high-level data replace low-level data to make it more meaningful and generalized.
27) What do you understand by Data Mining Interface?
The Data Mining Interface is used to improve the quality of the queries we use in Data Mining. It is nothing but a GUI form for Data Mining activities.
28) What do you understand by the term Cluster Analysis?
In the context of Data Mining, the term cluster analysis is an important type of analysis that is used in market research, pattern recognition, data analysis, and image processing, etc.
29) What are Interval Scaled Variables?
The continuous measurement of linear scale is called Interval Scaled Variable. For example, height and weight, weather temperature, etc. We can calculate these measurements by using Euclidean distance or Minkowski distance.
30) What are the most significant advantages of Data Mining?
There are many advantages of Data Mining. Some of them are listed below:
Because of the above reasons, Data Mining has become very popular nowadays and used by numerous industries, including marketing, advertising, IT/ITES, business intelligence, and even government intelligence organizations.
31) What are the most significant disadvantages of Data Mining?
Besides a lot of advantages, Data Mining has some disadvantages too. Following is the list of some of them:
Security is the biggest issue of Data Mining. Companies have information about their employees and customers, including social security numbers, birthdays, payroll, etc. However, this is always in the question that how they take care of this information. Hackers can access and steal customers' information, including personal and financial information, and may misuse it.
Due to Data Mining, concerns about personal privacy have been increasing enormously recently, especially in the age of the internet with social networks, e-commerce, online banking, etc. People can lose their personal and confidential information, which can cost them big troubles.
Misuse of information/inaccurate information
Data Mining doesn't ensure you give the correct information always. Information collected through Data Mining can be intended for ethical purposes and be misused. Hackers or unethical businesses can exploit people by using this information.
32) Which are the main prominent fields and areas where Data Mining is used?
Data Mining is mainly used in the following fields:
Finance & Banking Sectors
Data Mining is very important in the finance & banking field because data extraction provides financial institutions information on loans and credit reports. It facilitates us to create a model for historic customers by determining their good or bad credits. It is also used to detect fraudulent transactions by credit cards that protect a credit card owner.
Marketing & Retails
Marketing companies use Data Mining to create models based on the shopping history of their customers. By using this technique, they can sell profitable products to their targeted customers.
Increasing Brand Loyalty
Companies use Data Mining techniques in marketing campaigns after understanding their customers' needs and habits. After getting the right information, the companies can quickly increase their brand loyalty.
Helps in Decision Making
Companies use Data Mining techniques to help them in making some decisions in marketing or business. By using this technology, it is effortless to determine all information. Also, the company can decide what is unknown and unexpected.
To Predict Future Trends
Data Mining can be used to predict future trends by studying the data patterns for a long time. It can also help people to adopt behavioral changes.
Increase Company Revenue
Data mining technology involves collecting information on goods sold online. This can eventually reduce the cost of products and increase the company revenue.
Determining Customer Groups
Data Mining provides market analysis so we can get a response directly from customers. It also includes information during the identification of customer groups.
Increases Website Optimization
Data Mining can find all kinds of unseen element information, which can help you optimize your website.
33) What are the required technological drivers in Data Mining?
In Data Mining, we have to deal with mainly two things, database size, and query complexity.