MSBI : BI # 22 : Business Intelligence – Tools & Theory # 14 : Introduction to Data Mining #1 : Definition of Data Mining & How Data Mining works?

Hi Folks,

This post is part of Series Business Intelligence – Tools & Theory

Currently running topic for this series is listed as below :

Series Business Intelligence – Tools & Theory

>>Chapter 1 : Business Intelligence an Introduction

>>Chapter 2 : Business Intelligence Essentials

>>Chapter 3 : Business Intelligence Types

>>Chapter 4 : Architecting the Data

>>Chapter 5 : Introduction of Data Mining<You are here>

Continuing from my previous post on this series, If you have missed any link please visit link below

We are going to Cover the Following Points in this article

  • Introduction to Data Mining
  • Definition of Data Mining
  • How Data Mining works?
  • Types of relationship

Introduction to Data Mining

By now you must be familiar with the types of data, different data models, Meta data, granularity of data and data partitioning. This unit familiarizes you with the concept and functionalities of data mining.

Data Mining (DM) is a term that has been used to refer to the process of finding interesting information in large repositories of data. The term refers to the function of particular algorithms in a process from various disciplines like figures, database science etc. DM techniques are the effect of extended process of research and product development. This expansion began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. We will also study about the advantages, disadvantages, and ethical issues related to Data Mining.

DM is about solving problems by analyzing data which are already present in databases. Data mining is an influential new technology with great potential to help companies focus on the most important information in their data warehouses. Most organizations already gather and filter enormous quantities of data. Data mining techniques can be implemented quickly on accessible software and hardware platforms to improve the worth of accessible information resources.

Definition of Data Mining

We learnt in the introduction about an overview of data mining. Now let us know the definition of it. Data mining refers to extracting or pulling out the knowledge from large amounts of data. DM (sometimes called data or Knowledge innovation) is the process of analyzing data from diverse perspectives and summarizing it into useful information. This Information can be used to increase income, reduce costs, or both.

DM software is methodical tools for analyzing data. It allows users to examine data from many different proportions or angles, classify it, and summaries the relationships Data mining is about solving difficulties by examining data already present in databases. It consists of collecting and managing data it also includes investigation and forecast.

Data mining parameters

DM involves the utilization of complex data analysis tools to find out previously unknown, valid patterns and relationships in large data sets. These tools can include statistical models, arithmetic algorithms, and

machine learning methods (algorithms that improve their performance automatically through experience, such as decision trees).

DM applications can use a variety of parameters to examine the data. It is the process of extracting patterns from data.

The parameters included are:

· Association: Where one event is linked to another event, such as purchasing a pen and purchasing paper.

· Sequence or path analysis: Where one incident leads to another event, such as buying a car and filling fuel in it.

· Forecasting: Finding out patterns from which one can make sensible predictions regarding potential activities, such as the estimation about the people who join an athletic club may take exercise classes.

Many systematic tools utilize a verification-based approach, where the user develops a proposal and then tests the data to prove or disapprove the suggestion.

Example, a user might imagine that a customer, who buys a hammer, will also buy a box of nails. The efficiency of this approach can be limited by the imagination of the user to develop various conclusions, as well as the arrangement of the software being used.

In contrast, data mining uses a discovery approach, in which algorithms can be used to examine several multidimensional data relationships simultaneously, identifying those that are unique or frequently represented. In comparison to an expert system (which draws inferences from the given data on the basis of a given set of rules) data mining attempts to find out the hidden rules underlying the data. This is also called “Data Surfing”.

How Data Mining works?

In the earlier section we studied about the definition and the parameters of DM. Now we will learn how it works. The technique that is used to perform these skills in data mining is called „Modeling‟. Modeling is the act of constructing a model in a situation, where you know the answer and then applying it to another unknown situation. Computers are loaded with lots of data about a range of situations where an answer is known and then the data mining software on the computer must run through that data and condense the description of the data that should go into the model. Once the model is constructed it can then be used in similar situations where the answer is unknown.

Types of relationship

While large-scale information technology has been developing independently in transaction and analytical systems, data mining provides the link between these two systems Data mining software analyses relationships and patterns in stored transaction data based on open-ended user queries. There are different types of systematic software available Example, statistical, machine learning, and neural networks. Generally, four types of relationships are there:

· Classes: Saved data is used to position data in prearranged groups. For example, a restaurant chain could extract customer purchase data to determine when customers visit and what they typically order. This data could be used to increase and sustain customers by having daily specials.

· Clusters: Information is grouped according to rational associations or consumer preferences. For example, data can be extracted to identify market segments or consumer affinities.

· Associations: Data can be mined to identify associations. The car- petrol is an example of this.

· Sequential patterns: Data is extracted to foresee behavior patterns and trends. For example, an outdoor equipment vendor could predict the possibility of a backpack being purchased based on a consumer’s purchase of sleeping bags and hiking shoes.

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