data mining presentation

Data Mining

Ahmet Fahri Kılıçb101316014

Concepts Evolution Step Why data mining? What is data mining? Data Mining Process A Simple Data Mining Example Conclusions

Evolution Step 1960s Data Collection

What was my total revenue in the last five years? 1980s Data Access

What were unit sales in New England last March? 1990s Data Warehousing

What were unit sales in New England last march? Drill down to Boston

Today Data Mining What’s likely to happen to Boston unit sale next month?

Why?

Why Data Mining? Evolution of database technology:

To collect a large amount of data primitive file processing

To store and query data efficiently DBMS

New challenges: huge amount of data, how to analyze and understand? Data mining

Why data mining?

Large number of data (attributes and/or instances)

Need to take decision from them When size of data increases, information can

be gain easily from them are decreases Not to automate decision but the process itself

must be automatic or semi-automatic

What Is Data Mining? Data Mining An information activity that extracts

Facts Useful information Patterns

from data in large databases.

Process of discovering “knowledge/pattern” in data

What is Data Mining?

“Data Mining can be described as the non-trivial process of extracting previously unknown, interesting, potentially useful and ultimately understandable knowledge from huge datasets.”

-- Usama Fayyad

Knowledge Discovery Knowledge Discovery in Databases

(KDD) “The non-trivial process of identifying valid,

novel, potentially useful, and ultimately understandable patterns in data.”

KDD Process (iterative and interactive) Identifying the problem Preparing the data Building the model (data mining) Using and monitoring the model

The KDD Process

Data

Target data

Preprocesseddata

Transformeddata

Patterns

Knowledge

Selection Preprocessing

Transformation

Data mining

Interpretation/evaluation

Data Mining Process Preprocessing

Data Cleaning handles noisy, errors, missing,

irrelevant data Data Integration

multiple, heterogeneous data integrated into one

Data Mining Process 1. Data Selection

Identifying the data to be mined Choosing proper input attributes

and output information

Data Mining Process 2. Data Transformation

Form appropriate format Organizing and converting data in

desired ways Reducing the dimensionality of the

data Normalizing data

Data Mining Process 3. Data Mining

Implementing techniques to extract patterns of interest

Data Mining Algorithms: Decision Trees Neural Networks Rule Induction Nearest Neighbor Genetic Algorithms

Data Mining Process 4. Pattern evaluation

Where the mined data are being tested and assessed for understanding the synthesized knowledge and its range of validity

Data Mining Process 5. Knowledge presentation

Presenting the results to the decision-maker

Imagine a company that solicits business primarily by mailing advertisements:

• This company has compiled a store of data containing information about the customers receiving these ads, and the response history.

• This database could then be mined to discover trends, patterns, or systematic relationships that reveal some identifying characteristics of customers who responded favorably.

• With this knowledge, future advertisement mailings could be directed only to new customers with these characteristics.

A Simple Data Mining Example

A Simple Data Mining ExamplecustomerID gender birthdate city state response045 F 01/08/69 Benicia CA Y

678 M 07/13/65 Dallas TX N

256 F 10/21/72 Boston MA Y

customerID gender birthdate city state response024 M 10/23/48 Bethesda MD

098 M 4/21/62 Lincoln NE

781 F 12/21/76 Tucson AZ

data mining identifying characteristics of responsive customers