Data Analytics

Data Mining Techniques, Concepts, and Its Application

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Preface

With increasing dependency on data and efficient utilization of databases, data mining is highly used in data science to promote the extraction and discovery of patterns in massive datasets. In this article, data mining will be covered in detail.

Introduction

Data mining is growing ever more popular with an increasing focus on datasets to facilitate machine learning and other data science applications involving analytics and predictive analysis. It allows data scientists to find trends and patterns inside datasets alongside anomalies to predict outcomes and forecast. Data mining has an immense contribution in accurately predicting events and acknowledging future occurrences, indirectly increasing the profitability of companies and minimizing loss, risk, and waste of resources. We will discuss data mining concepts and techniques more extensively and the purpose of using data mining in the first place in this article.

About AnalytixLabs

AnalytixLabs hosts many comprehensive courses that focus on data mining techniques and other IT or data science components, focusing on practical well-orchestrated learning modules for coaching and preparing field-ready data scientists. AnalytixLabs is one of India’s leading applied AI and data science training institutes, which caters to these specialized fields. 

1. Data Mining Meaning

Data mining is the process used for extracting usable data from datasets. It refers to finding patterns in data inside datasets or massive batches of data using data mining tools or software. So, if asked about data mining meaning, one can relate it to discovering information inside data. Data mining is also referred to as knowledge discovery in data (KDD).

2. Data Mining Concept

Data mining is a field of data science and computer science geared towards extracting information from datasets using statistical methodologies. The extracted data can be used later on to promote various functions. The mined data is transformed and can be filtered out from datasets to only include usable information. 

3. Data Mining Techniques

Data mining relies on various techniques and tools, ranging from database management, and statistics to machine learning. These, combined with the data mining concept and techniques, allow data mining processes to extract valuable data and make predictions from massive amounts of data. These techniques help in functions like cleaning noise with data cleaning techniques in data mining or classifying data into categories with classification techniques in data mining. Here are some of them:

Data Miming Techniques
  • Tracking patterns – One of the fundamental techniques of data mining involves recognizing patterns inside datasets. This recognizes abnormalities and aberrations from data in relation to time and other variables. This can be later used to create patterns from data trends. For instance, the demand for an item might increase exponentially during a certain time period.
  • Classification – This is a more complex data mining technique that involves collecting multiple attributes at once into different classifications. One can then focus on singular or targeted classifications to extract further information or conduct advanced analytics. For instance, during the data evaluation of employees, one can classify them as upper level, middle level and lower level employees which can then be used to analyze targeted employees from various departments.
  • Association – It is fundamentally connecting variables and elements to each other through data-centric conclusions. Association uses various events and attributes which are proportional or related in nature and then comes to conclusion based on that information. With association, one might notice that a specific wireless earphone is bought alongside a particular smartphone. And, this event is occurring among multiple other customers and then use that information for various things in the future such as predicting sales and managing inventory.
  • Outlier detection – This data mining technique involves the recognition of aberrations and anomalies to prove a better understanding of datasets. This helps in predicting future events and promotes efficient and effective management of challenges. This technique helps acquire profit from planned future events armed with informative data gathered from past or current anomalies. For instance, for some reason, customers start buying more Coca-Cola than Thumbs Up in India in the month of July. While using this technique for the investigation of the causes of the switch, one can make a better profit based on that information, especially if he/she can use that anomaly to his/her advantage to replicate specific events.
  • Clustering – This technique is quite similar to Classification and involves assimilating chunks of data based on what they have in common. One can use this technique to combine chunks of various demographics or elements based on their attributes which further help in creating targets. For instance, one might use clustering to collect all the people in their 20s and 50s who have similar preference while buying a car, for example. To learn more, read Types of Clustering Algorithms in Machine Learning With Examples.
  • Regression – This data mining technique is used for modelling and planning based on forecasted events and various variables. Regression technique is mainly involved in uncovering the connection and relationship between more than one variable belonging to a dataset.  For example, Linear Regression in ML can be used for the projection of sales, demand, price and other elements such as competition, complementary products and other economical factors.
  • Prediction – This is one of the main techniques in data mining that allows the projection of future data and to forecast events. This is done by analyzing historical patterns and trends. For instance, one can use Prediction to analyze the spending of a client to forecast his or her future spending. To learn more, read about What is Business Forecasting And Its Methods?

4. Purpose of Data Mining Techniques

Organizations and individuals use data mining to convert raw unmined data into valuable information. Data mining helps users and companies gain profit or decrease risk by predicting inventory and demand needs or taking advantage of variables that will promote sales or reach. It utilizes tools and methodologies to search patterns and trends among large batches and chunks of data. This allows businesses and corporations to make better business decisions or tackle challenges better and avoid risk. 

Data mining is fundamentally used to search for aberrations, trends, and correlations within datasets to forecast outcomes. This fulfills the primary requirement of a predictive model and helps businesses maximize profits. Data mining can help in many sectors such as customer management, employee management, resource management, and financial analytics and analysis. Data mining contains these very instrumental techniques, out of which every method plays an important role or has a necessary function it serves.

You may also like to read: Difference Between Data And Information? Data Vs Information

5. Applications of Data Mining

Data mining has many applications across hundreds of fields and in thousands of projects now. Here are some real-world applications of data mining that can be observed in daily commercial and industrial applications:

Applications of Data Mining
  1. Manufacturing and engineering processes

Data mining tools prove to be quite valuable in discovering patterns in production and manufacturing. Data mining allows users to use data mining tools to extract relationships between production and product architecture. Also, it helps in extracting information that can be used in system-level designs. Data mining allows product development duration, cost, and values of other variables to be predicted.

  1. Customer segmentation

Data mining allows the classification of customers based on buying behavior or demographics, which further lets businesses target customers much more effectively without wasting unnecessary resources. Data mining classifies customers into various segments, which helps companies tend to these classifications independently. 

  1. Financial services and banking

With most financial services being hosted on the cloud and computerized, digitized financial records and information are generated every second. This would be a challenge to maintain and compile for banking institutions and financial services without data mining. These financial organizations have specialized requirements that demand effective recognition of data and variables. Data mining helps in maintaining various services like loans, market investments, and financial records. 

  1. Research and analysis

Data mining is very beneficial during data cleaning, pre-processing data, and database integration. Many aspects of data mining and various tools can be effectively utilized to conduct research and analytics accurately based on available data and generate or extract data from multiple sources such as data warehouses or historical and records. 

  1. Criminal investigation

Criminology is a process that is aimed at identifying crimes. Criminal analysis and crime analytics find heavy utility in data mining to help determine crime characteristics and historical records to recognize patterns, solve crimes and make predictions based on criminals and criminal activity. 

Conclusion

Data mining is a precious method of finding the required patterns and abnormalities from datasets and data. Data mining techniques are highly used to conduct massive research and analytics to figure out potential or probable information about specific future events and the best courses of action to take once facing them or the requirements to be met to harness better profit or gain desired outcomes. In the coming years, data mining will be more extensively used to serve the common function of analyzing, associating, and predicting to help businesses and smaller industries further. With growing industry demands and a dire need for data-centric predictive forecasting, data mining will keep enjoying the spotlight for years to come, especially in the fields of computer science and data science.

6. FAQs- Frequently Asked Questions

Q1. What are data mining tools?

Data mining tools are software which alongside data mining and statistical techniques, are used to power data mining processes. Some of the highly effective data mining tools are SQL, R-Programming, and Python.

Q2. Which software is used for data mining?

Software such as KNIME, Rapid Miner, PowerBI, Apache Hadoop, HIVE, Apache Spark, and Teradata are extensively used for data mining.

Realted: 10 Free Data Analytics Tools | 16 Best Big Data Analytics Tools And Their Key Features

Q3. What is a data mining job?

Data mining jobs consist of using the aforementioned data mining techniques and tools to predict outcomes for businesses and companies by searching for patterns inside the large batches of datasets. Read this to know more about Data Analyst Salary, Skills, and Responsibilities.

Q4. Which techniques in data mining give the best performance?

Every technique has some advantages and shortcomings. Based on the problem statement, one has to pick an appropriate method, and sometimes, an analyst may need to test out multiple approaches to meet the project objectives. However, clustering, regression, and classification are the most widely used methods that serve the purpose well majority of the time.

You may also like to read: How to Choose The Best Algorithm for Your Analytics Problems?

Q5. What are some real-world data mining examples?

Marketing is heavily dependent on data mining, primarily digital or online marketing. Customers are targeted and recognized using data mining. Banks and creditors use data mining as well before providing credit or loans to customers. Data mining allows banks to access the probability of the customer paying the loan and then disbursing the loan amount or rejecting the application.

Q6. What is data cleaning?

Data cleaning techniques in data mining are highly necessary to facilitate a smooth mining process. Data cleaning prepares the batches of data or data from datasets for the process of data mining before the valuable or required information can be extracted from the dataset. Data Acquisition in Machine Learning and data cleaning steps are critical to ensure that end results deliver are meaningful and actionable.

Q7. What is data warehousing?

Data warehousing is a process of centralizing the source and sending data from multiple sources into a data warehouse or common repository. It is a highly crucial step in Big Data Engineering Value Chain.

Q8. What are the disadvantages of data mining?

The disadvantages of data mining are factors such as security, privacy, and misuse of information. As businesses become overly dependent on personal data, there is an increasing threat of egregious privacy violations. Data mining also leads to irrelevant information and inaccurate data at times.

Q9. What are Classification techniques in data mining?

Classification techniques in data mining are supervised learning techniques where the classifier analyzes the training dataset to build classification rules. This helps in predicting the values of unknown attributes. There are various methods for classification in machine learning to handle complex and large volumes of datasets. Classification involves the prediction of events or outcomes based on given inputs.

You may also like to read: 

1. Difference Between Data Analysis and Interpretation – An Overview

2. Machine Learning Vs. Data Mining Vs. Pattern Recognition

3. Why Should You Learn SQL in The Age of Python & R?

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