Data Mining Tutorial

  • Data mining is one of useful techniques that help entrepreneurs, researchers, and individuals to extract valuable information from huge sets of data. Data mining is also called as Knowledge Discovery in Database (KDD).
  • Knowledge Discovery Process includes Data cleaning, Data integration, Data selection, Data transformation, Data mining, Pattern evaluation, and Knowledge presentation.

What is Data Mining

  • The process of extracting information to spot patterns, trends, and useful data that would allow the business to require the data-driven decision from huge sets of data is named data processing .
  • Data Mining is a process used by organizations to extract specific data from huge databases to solve business problems. It primarily turns raw data into useful information.
  • Data mining is that the analysis step of the "knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.
 Data Mining Introduction

Data Mining Introduction

Types of Data Mining

  • Data mining can be done on the following types of data:
    • Relational Database
    • Data warehouses
    • Data Repositories
    • Object-Relational Database
    • Transactional Database

Relational Database

  • A collection of multiple data sets formally organized by tables, records, and columns from which data can be accessed in various ways without having to recognize the database tables is called relational database.
  • Tables convey and share information, which facilitates data search ability, reporting, and organization.

Data warehouses

  • The technology that collects the data from various sources within the organization to provide meaningful business insights is called as Data Warehouse.
  • Large amount of data comes from multiple places such as Marketing and Finance. The extracted data is utilized for analytical purposes and helps in decision- making for a business organization. The data warehouse is designed for the analysis of data rather than transaction processing.

Data Repositories

  • Data Repository refers to a destination for data storage.

Object-Relational Database

  • Combination of an object-oriented database model and relational database model is called an object-relational model. It supports Classes, Objects, Inheritance, etc.
  • Primary objectives of the Object-relational data model is to close the gap between the Relational database and the object-oriented model practices frequently used in many programming languages, for example, C++, Java, C#, and so on.

Transactional Database

  • This refers to a DBMS that has the potential to undo a database transaction if it is not performed appropriately. A unique capability a very long while back, today, most of the relational database systems support transactional database activities.

Advantages of Data Mining

  • Enables organizations to obtain knowledge-based data.
  • Enables organizations to make lucrative modifications in operation and production.
  • Cost-efficient.
  • Facilitates the automated discovery of hidden patterns.
  • induced in the new system as well as the existing platforms.
  • It is a quick process.
  • Determining Customer Groups.
  • Increases Brand Loyalty.
  • Helps in Decision Making.

Disadvantages of Data Mining

  • Selection of the right data mining tools is a very challenging task.
  • Data Mining techniques are not precise lead to severe consequences in certain conditions.
  • Organizations may sell useful data of customers to other organizations for money.
  • Many data mining analytics software is difficult to operate.

Data Mining Applications

  • It is used by organizations with Intense consumer demands- Retail, Communication, Financial, marketing company, determine price, consumer preferences, product positioning, and impact on sales, customer satisfaction, and corporate profits.
 Data Mining Applications

Data Mining in Healthcare

  • It uses data and analytics for better insights and to identify best practices that will enhance health care services and reduce costs.

Data Mining in Market Basket Analysis

  • This technique may enable the retailer to understand the purchase behavior of a buyer. Data assist retailer in understanding the requirements of the buyer and altering accordingly.

Data mining in Education

  • Education data mining is currently emerging field, concerned with developing techniques that show knowledge from the data generated from educational Environments.

Data Mining in Manufacturing Engineering

  • Data mining can be used in system-level designing can also be used to forecast the product development period, cost, and expectations among the other tasks.

Data Mining in CRM (Customer Relationship Management):

  • It is all about obtaining and making Customers, also enhancing customer loyalty and implementing customer-oriented strategies.

Data Mining in Fraud detection

  • Traditional methods of fraud detection are a little bit time consuming. An ideal fraud detection system should protect the data of all the users. A Model is constructed using this data, and the technique is to identify that the document is fraudulent or not.

Data Mining in Lie Detection

  • Data collected from the previous investigations is compared, and a model for lie detection is constructed.

Data Mining Financial Banking

  • It Can help bankers by solving business-related problems in banking and finance by identifying trends, casualties, and correlations in business information and market costs that aren't instantly evident to managers or executives because the data volume is just too large or are produced too rapidly on the screen by experts.

Challenges of Implementation in Data mining

  • Process of data mining becomes effective when the challenges or problems are correctly recognized and adequately resolved.
 Challenges in Data Mining

Challenges in Data Mining

Incomplete and noisy data

  • Problems may occur due to data measuring instrument or because of human errors.
  • Data could get changed due to human or system error. These consequences make data mining challenging.

Data Distribution

  • It is a quite tough task to make all the data to a centralized data repository mainly due to organizational and technical concerns.
  • Data mining requires the development of tools and algorithms that allow the mining of distributed data.

Complex Data

  • Managing these various sorts of data and extracting useful information may be a tough task.
  • Most of the time, new technologies, new tools, and methodologies would need to be refined to get specific information.


  • If the designed algorithm and techniques aren't up to the mark, then the efficiency of the data mining process are going to be affected adversely.

Data Privacy and Security

  • Usually results in serious issues in terms of knowledge security, governance, and privacy.

Data visualization

  • Data visualization may be a vital process because it's the first method that shows the output to the user during a presentable way.
  • The extracted data should convey the precise meaning of what it intends to precise. But repeatedly, representing the knowledge to the end-user during a precise and straightforward way is difficult.

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