Data Mining

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DATA MININGDayanand Academy of Management Studies

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Contents

Data Mining Introduction1

Data Mining Procedures2

Data Mining Techniques3

Data Mining Application4

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Data Mining


Introduction


Intoduction

What is Data Mining?

Data mining is the process of extracting meaningful piece of information from Data warehouses , which can be useful for maximizing profit , fraud detection , marketing perspective and scientific research.

Data Warehouses: According to Stanford University,

"A Data Warehouse is a repository of integrated information, available for queries and analysis. Data and information are extracted from heterogeneous sources as they are generated .This makes it much easier and more efficient to run queries over data that originally came from different sources."



Data Minining Steps

Fourth Step

Third Step

Second Step

First Step

Knowledge Deployment

Model Building

Data Gathering

Problem Definition

Data Mining Procedures

Problem Definition:- Data mining project focuses on understanding the

objectives and requirements of a particular project of business. The Project must be specified from a business point of view. After that it can be formulated as a data mining problem and develop a preliminary.

Data Gathering & Preparation:- This task involves data collection and exploration. It can

be done by Removing unnecessary information , Detecting Data Duplicity and supplying some new information.


Data Mining Procedures

Model Building and Evaluation:- In this phase, various Modeling Techniques can be applied

to build the data model which is likely to be sufficient with the requirement and then An Evaluation can be done to compare the current model with the originally stated project goal.

Knowledge Deployment:- Knowledge deployment is the use of data mining within a

target environment. In the deployment phase, insight and actionable information can be derived from data.


History of Data Mining Techniques

1950•Neural Networks•Clustering

1960’s•Decision Trees

1980’s•Support Vector Machine


1999 •Cross Industry Standard Platform Data Mining Package (Crisp DM 1.0)

2004 •Java Data Mining Package (JDM 1.0)

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Data Mining


Neural Networks

Neural Networks:-

Neural networks are non-linear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. Using neural networks as a tool, data warehousing firms are extracting information from datasets in the process known as data mining.

Neural network is a techniques derived from artificial intelligence research that uses generalized regression and provide methods to carry it out.

It is self adapted and it uses learning method.


Processing of Neural Networks

Input data is presented to the network and propagated through the network until it reaches the output layer. The predicted output is subtracted from the actual output and an error value for the networks is calculated through supervised learning.

Once back propagation has finished, the forward process starts again, and this cycle is continued until the error between predicted and actual outputs is minimized.


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Data Mining


Clustering

Clustering

Clustering is used to segment the data. Clustering models segment records into groups that are similar to each other which is totally distinct from other groups.

Typical Applications of Clustering are Online Document Classification and to cluster web log data to discover groups of similar access patterns. Pattern Recognition, Spatial Data Analysis and Image processing are other applications in Scientific areas.


Clustering


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Decision Trees

Decision Trees

The Decision Tree algorithm is based on conditional probabilities. Decision trees generate rules. A rule is a conditional statement that can easily be understood by humans and easily used within a database to identify a set of records.

The Decision Tree algorithm produces accurate and interpretable models with relatively little user intervention. The algorithm can be used for both binary and multi-class classification problems.


Decision Trees


Node 1 sows about married persons and 0 describes single persons. Node 1 has 712 records (cases). Of these, 382 have a target of 0 (not likely to increase spending), and 330 have a target of 1 (likely to increase spending).

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Support Vector Machines

Support Vector Machine

An optimal Defined Surface.Linear and non linear Input Space.Linear or High Dimension Feature Space which

is specially defined Kernel function.SVM involves the fitting of a hyper plane such

that the largest margin is formed between 2 classes of vectors while minimizing the effects of classification errors so that we can classified in to groups.


Support Vector Machine


Support Vector Used For

Classification

Regression

Unsupervised Learning and supervised learning.


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Data Mining


JAVA Data Mining

JAVA Data Mining


Facilities by JDM 1.0 Package


Parallel Processing


Distributed Computing


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Data Mining


Cross Industry Standard Platform

Data Mining

Crisp DM 1.0

Business Understanding

Data Understanding

Data Preparation

Data Modeling

Evaluation

Deployment


Data

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Data Mining

Data Mining Applications




Data Mining

BusinessOnline Searching

Science

Marketing

Spatial Data Mining

Security


BUSSINESS PRECPECTIVE:-

Data mining helps business to extract information from resources such as print media, television, internet, investment. Data mining tools predicts future trend and behavior allowing business to make proactive knowledge driven decision for increasing revenue, profit of the company.

SCIENCTIFIC PRECPECTIVE:- Practical perspective describe how techniques from

data mining can be used to address and resolve the modern problem in science and engineering domains.



SECURITY PRECPECTIVE:-

To prevent or detect for fraud such as showing wrong geographical domain and to identify stolen credit card by transaction history. Data Mining can help to make online transactions more secure and reliable by analyzing previous transaction records.

SPATIAL DATA MINING:-

Geo-marketing companies doing customer segmentation based on spatial location through data mining by mining the purchase and subscription history .


WEBSITE PROMOTION:-

Web owner can attract most number of visitors by mining their data and then modifying their layout on the basis of extracted information.


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