Top 10 Data Mining Techniques in 2021: A Detailed Guide

Introduction

Data mining, first time hearing it? If you are a novice in the business environment it is highly unlikely that you’ve heard of this word before. Data Mining is mostly used in banks and companies. It began in the 1990s and it has come directly from the evolution of data warehouse and database technologies. Read on further to know what data mining purports to do and what are data mining techniques.

1. What is Data Mining?
2. What are data mining techniques?
3. Types of Data mining techniques:

1) What is Data Mining?

In layman’s terms, Data mining is a technique of segregating data from huge data sets to find to find unknown relationships or patterns which were present previously in them. It is a process of extracting useful data from a larger set of raw data. It is all about discovering unsuspected, hidden, previously unknown yet valid relationships present amongst the data. Data mining is a very powerful tool that helps you find relationships and patterns within your data. The main properties of data mining are:

1. Automatically discovering patterns.
2. Predicting likely outcomes.
3. Creating actionable information.
4. Focusing on large databases and data sets.

Now as the meaning of data mining is clear let’s move on to an overview of data mining techniques. 

2) What are data mining techniques?

In recent projects, data mining concepts and techniques have been used and developed including classification, association, prediction, clustering, sequential patterns, and regression. In today’s digital world even though we are drowning in and are surrounded by a big amount of data and information that is forecasted to enlarge by 40% into the coming decade, what’s ironic is that we are starving for knowledge.

Due to the generous amount of data produced which is present today it is difficult to put it to use as it is failing to satisfy big initiatives and that is why data is mined using data mining techniques. There are various data mining tools and techniques providing a different insight with each and catering to a specific business problem. Data mining techniques and applications must be reliable and repeatable by company individuals who have little or no knowledge about data mining and its techniques. 

3) Types of Data mining techniques:

The different types of data mining techniques with examples are:

• Classification: The classification of data mining substructures is used to retrieve relevant and important information about metadata, and data. The data is classified and segmented into different classes or segments in this technique. The classification by this data mining technique can be done by different criteria:
• As per the types of data sources: This classification is done according to the type of data handled. Examples include spatial data, multimedia, text data, World Wide Web, and so on.
• As per the database which is involved: The classification is done based on the data model which is involved. Examples of this include Object-oriented databases and relational databases.
• As per the type of knowledge discovered: This classification depends on data mining functionalities and the kind of knowledge discovered from different researches. Examples include clustering, characterization, and discrimination classification.
• As per the different data mining concepts and techniques used: The classification is as per the utilized data-analysis approach like genetic algorithms, machine learning, neural networks, statistics visualization, etc.
• Clustering: To identify data that is alike the clustering data mining technique is used. The process aids to understand similarities and differences in data. This technique is fairly similar to classification but the former involves chunks of data being grouped together on a similarity basis. Examples of clustering are text mining, computational biology, medical diagnostics, spatial database applications, etc.
• Regression: You must have certainly heard of this technique before. One of the most common types of data mining technique, regression analysis is used to analyze and identify the relationships between variables due to the other factor being present. The probability of a specific variable is being defined in this technique. It is used to recognize the possibility of a particular variable, given the existence of other variables.
• Association Rules: As the name suggests this technique helps to find the association and alliance between two or more items. These include if-then statements that show the chance of interactions in different types of databases between data items. The three major measurement techniques include lift, support, and confidence.
• Outer detection: This kind of data mining technique is related to the scrutiny of data items in the data set which do not complement an expected behaviour or pattern. It is also called Outlier mining or Outlier analysis. The outlier is known as a data point that disunites too much from the database. Outlier detection plays a very significant role in the field of data mining. It is applicable in numerous fields like credit or debit card fraud detection, network interruption identification, and detection of outlying in wireless sensor network data. 
• Sequential patterns: This criterion of data mining techniques and algorithms helps to identify or discover similar trends or patterns in transaction data for a certain period. This technique specializes in evaluating sequential data to discover consecutive patterns. 
• Prediction: This technique includes a combination of other techniques like clustering, sequential patterns, trends, classification, etc. It scrutinizes past instances or events in the right sequence to predict a future event.

Conclusion 

Data mining methods and techniques benefit both the companies and organizations to get knowledge-based information and adjust operations and productions profitably. It is cost-efficient and effective and helps with the decision making process in the company. New as well as existing platforms can implement it. It is a speedy process making it easy for the users to analyze and interpret huge data in less time. However, data mining analytics software is complex to operate and needs training in advance. The selection of the right data mining tools and techniques is a very difficult task.

Data mining is applicable in many diverse industries like Bioinformatics, supermarkets, e-commerce, service providers, retail, banking, crime investigation, manufacturing, education, insurance, and communications. The popularly used data mining tools in the industry are R-language and Oracle data mining. The implementation process of data mining includes business understanding, data understanding, and preparation, modeling, evaluation, and deployment. Data mining should be taken up more and more in order to make your company or organization work efficiently and profitably.

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