This means there will be lots of work opportunities in AI and ML. Data Mining is an integral component of both. You must have a strong foundation in Data Mining. We answer some of the queries asked in the data mining interview questions.
Data Mining is the process of converting raw data into useful insights that can be used for business and organizational purposes. Data Mining includes data and database management, data validation, data updating online, data discovery, and the discovery of valuable patterns within complex datasets.
Data Mining is essentially about the automated analysis of large amounts of data in order to find hidden patterns and insights. If you are looking for a dream job in AI/ML, you will need to be able to answer all Data Mining questions asked by your interviewer.
We’ve compiled a list of the most frequently asked Data Mining interview questions. This list covers all levels and concepts of Data Mining interview questions that every AI/ML aspirant should know.
Let’s just get started!
1. Name the different Data Mining techniques and explain the scope of Data Mining.
The different Data Mining techniques are:
- Prediction – It determines the relationship between dependent and independent instances. If you want to predict future profits, for example, using sales data, the sale is an independent instance while the profit acts as the dependent instance. Based on historical sales data and profit data, the predicted profit is therefore calculated.
- Decision trees – A decision tree’s root is a condition or question that has multiple answers. Each answer leads you to data that aids in making the final decision.
- Sequential patterns – It is a method of analyzing transaction data to find similar patterns or events. A brand can identify patterns in transactions that occurred in the past year by using historical customer data.
- Clustering analysis – This technique automatically creates a group of objects with similar characteristics. The clustering method creates classes and places the appropriate objects within each class.
- Classification analysis – This ML-based method classifies each item in a set into predefined groups. This advanced technique uses linear programming, neural networks, and decision trees.
- Association rule learning – This creates a pattern that is based on the relationships of the items in one transaction.
The scope of Data Mining is to:
- Predict trends and behaviors – Data Mining automates the process for identifying predictive information from large data sets/databases.
- Discover previously unknown patterns – Data Mining tools scan and extract data from a wide range of databases in order to uncover hidden trends. This is a process of pattern discovery.
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2. What are the types of Data Mining?
Data Mining can be classified into the following types:
- Integration
- Selection
- Data cleaning
- Pattern evaluation
- Data transformation
- Knowledge representation
3. What is Data Purging?
Data purging is an important procedure in database management systems. This helps maintain the relevant data within a database. This refers to the removal or deletion of unnecessary NULL values from rows and columns. It is important to first purge any data that does not belong to the new data you are trying to load into the database.
You can quickly get rid of junk data by Data Purging your database. This will reduce the database’s performance.
4. What is the fundamental difference between Data Warehousing and Data Mining?
A data warehouse is a technique that extracts data from different sources. This is a data mining important question. The data is then cleaned up and stored for future reference. Data Mining, on the other hand, is the process of extracting data and querying it to analyze the results. Data Mining is crucial for reporting, strategy planning, visualization, and capturing valuable insights from the data.
5. Explain the different stages of Data Mining.
There are three major stages to Data Mining.
Exploration – This stage focuses on gathering data from multiple sources, and preparing it to be used for other activities such as cleaning and transformation. After the data has been cleaned up and transformed, they can be analyzed for insights.
Model Building and validation – This stage is about validating the data using different models and comparing them for the best performance. This is also known as pattern identification. This step is time-consuming as the user must manually determine which pattern is best for making predictions.
Deployment – Once the most suitable pattern for prediction has been identified, it can be applied to the data set for the purpose of estimating predictions or outcomes.
6. What is the use of Data Mining queries?
Data Mining queries allow you to apply the model to new data to produce single or multiple results. Queries are able to retrieve cases that match a specific pattern with greater ease. They can extract the statistical memory from the training data and help to obtain the exact pattern as well as the rule of the typical instance that represents the pattern in the model. In order to help explain patterns, queries can also extract regression formulas or other calculations. They can also retrieve information about individual cases in a model.
7. What are “Discrete” and “Continuous” data in Data Mining?
Data Mining refers to discrete data as data that is finite but has a meaning. Distinct data is best illustrated by gender. Continuous data is data that changes in a structured way. Continuous data is best illustrated by age.
8. What is OLAP? How is it different from OLTP?
OLAP (Online Analytical Processing), is a technology that is used in many Business Intelligence applications. It involves complex analytical calculations. OLAP can also be used to perform advanced data modeling and trends analysis. OLAP systems are used to reduce query response times and increase the effectiveness of reporting.
The OLAP database stores historical data in an aggregated form in a multidimensional structure. OLAP is a multidimensional database that allows users to see how data comes from different sources.
Online Transaction and Processing stands for OLTP. Because it is used for large transactions and large amounts of data, it is distinct from OLAP. These applications are most common in the BFSI industry. OLTP architecture can be used to support transactions across multiple networks.
Also read: 9 Required Skills to Become a Freelance Data Scientist in 2021
9. Name the different storage models that are available in OLAP?
There are many storage options available in OLAP:
- MOLAP (Multidimensional Online Analysis Processing) – This type of storage stores data in multidimensional cubes and not standard relational databases. This feature makes query performance exceptional.
- ROLAP (Relational online analytical processing) – This data storage is in relational databases and is therefore capable of handling large volumes of data.
- HOLAP (Hybrid Online Analytical Process) – This is a mixture of MOLAP/ROLAP. HOLAP uses MOLAP to extract summary information from the cube. However, for drill-down capabilities, it uses the ROLAP model.
10. What is Data Aggregation and Generalization?
Data Aggregation refers to the process of combining or aggregating data together to create a cube that can be used for data analysis. Generalization refers to the process of replacing low-level data with higher-level concepts in order to generalize the data and provide meaningful insights.
We hope that these Data Mining Interview Questions and their answers help you break the ice with Data Mining. While these are just some basic level data mining questions that you must know, they will help you to gain fluency and dig deeper into the topic.
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