Data Mining

BY

JEMINI ISLAM

Data Mining

Outline:

• What is data mining?

• Why use data mining?

• How does data mining work

• The process of data mining

• Tools of data mining

What is data mining?

Generally, data mining (sometimes called data

or knowledge discovery) is the process of

analyzing data from different perspectives and

summarizing it into useful information. It allows users to

analyze data from many different dimensions or angles,

categorize it, and summarize the relationships identified.

Technically, data mining is the process of finding correlations

or patterns among dozens of fields in large relational

databases.

Cont..

Data Mining, also known as Knowledge-Discovery

in Databases (KDD), is the process of automatically

searching large volumes of data for patterns. Data

Mining is a fairly recent and contemporary topic in

computing. However, Data Mining applies many

older computational techniques from statistics,

machine learning and pattern recognition.

Example of data mining:

A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make acorrelation between that customer and silk shirts. The sales department will look at that information and may begin direct mail marketing of silk shirts tothat customer, or it may alternatively attempt to get the customer to buy a wider range of products..

Example Cont..

Another widely used (though hypothetical) example

is that of a very large North American chain of

supermarkets. Through intensive analysis

of the transactions and the goods bought over a

period of time, analysts found that beers and diapers

were often bought together.

Continue..

The grocery chain could use this newly discovered

information in various ways to increase revenue.

For example, they could move the beer display

closer to the diaper display. And, they could

place the high-profit diapers next to the high-profit

beers.

Why use data mining?

• Data is one of the most valuable assets for any corporation - but only if we know how to reveal valuable knowledge hidden in raw data. Data mining allows us to extract diamonds of knowledge from historical data and predict useful outcomes form that.

Cont..

Data mining can-

* optimize business decisions,

* increase the value of each customer and

communication, and

*improve satisfaction of customer with your services.

How does data mining work?

Data mining creates link between separate

transactions and analytical systems in a large-

scale information technology. It uses various

software to analyze relationships and patterns.

Generally,the following four types of

relationships are sought:

Classification

A task of finding a function that maps records into one of several discrete classes. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

Clustering

Clustering is a task of

identifying groups of records

that are similar between

themselves but different from

the rest of the data. For example,

data can be mined to identify

market segments or consumer

affinities

Association.

Data can be mined to identify association.

The beer-diaper example is an example of

associative mining.

Sequential Patterns

Data is mined to anticipate

behavior patterns and trends.For

example, an outdoor equipment

retailer could predict the

likelihood of a backpack being

purchased based on a consumer's

purchase of sleeping bags and

hiking shoes.

The process of data mining

The process of data mining consistsof three stages: 1) The initial exploration, 2) model building or pattern identification with validation or verification, and (3) deployment (i.e., the application of the model to new data in order to generate predictions).

Stage 1: Exploration

This stage usually starts with data preparation which may involve cleaning data,data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") – performing some preliminary feature selection operations to bring the number of variables to a manageable range).

Stage 2: Model building and validation.

This stage involves considering various

models and choosing the best one based on

their predictive performance (i.e., explaining

the variability in question and producing

stable results across samples).

Stage 3: Deployment.

That final stage involves using the model

selected as best in the previous stage and

applying it to new data in order to generate

predictions or estimates of the expected

outcome.

Tools of Data Mining

Artificial Neural

Networks: Non-linear

predictive models that

learn through training

and resemble biological

neural networks in

structure.

Cont..

Genetic algorithms: Optimization techniques

that use processes such as genetic

combination, mutation, and natural selection

in a design based on the concepts of natural

evolution.

Cont..

Decision trees: Tree

shaped structures that

represent sets of

decisions. These

decisions generate rules

for the classification of a

dataset

Cont..(Tools of Data Mining)

Nearest neighbormethod: A technique thatclassifies each record in a dataset based on acombination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbortechnique

Cont..

Rule induction: The

extraction of useful if-

then rules from data

based on statistical

significance.

Tools of Data Mining (Cont..)

Data visualization: The visual interpretation

of complex relationships in multidimensional

data. Graphics tools are used to illustrate data

relationships.

Conclusion:

The concept of Data Mining is becomingincreasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty. Today increasingly more companies acknowledge the value of this new opportunity and use data mining tools and solutions that help optimizing their operations and increase customer’s bottom line.