Introduction to data mining technique

INTRODUCTION TO DATA MINING
TECHNIQUE
By – Pawneshwar Datt Rai

WHAT IS DATA MINING?
 Data mining is also called knowledge discovery and data
mining (KDD)
 Data mining is
 extraction of useful patterns from data sources, e.g.,
databases, texts, web, image.
 Patterns must be:
 valid, novel, potentially useful, understandable
This PPT presented By - Pawneshwar Datt Rai

EXAMPLE OF DISCOVERED PATTERNS
 Association rules:
“80% of customers who buy cheese and milk also buy
bread, and 5% of customers buy all of them together”
Cheese, Milk Bread [sup =5%, confid=80%]

MAIN DATA MINING TASKS
 Classification:
mining patterns that can classify future data into known
classes.
 Association rule mining
mining any rule of the form X  Y, where X and Y are
sets of data items.
 Clustering
identifying a set of similarity groups in the data

MAIN DATA MINING TASKS
 Sequential pattern mining:
A sequential rule: A B, says that event A will be
immediately followed by event B with a certain confidence
 Deviation detection:
discovering the most significant changes in data
 Data visualization: using graphical methods to show
patterns in data.

WHY IS DATA MINING IMPORTANT?
 Rapid computerization of businesses produce huge
amount of data
 How to make best use of data?
 A growing realization: knowledge discovered from
data can be used for competitive advantage.

WHY IS DATA MINING NECESSARY?
 Make use of your data assets
 There is a big gap from stored data to knowledge; and
the transition won’t occur automatically.
 Many interesting things you want to find cannot be found
using database queries
“find me people likely to buy my products”
“Who are likely to respond to my promotion”

WHY DATA MINING NOW?
 The data is abundant.
 The data is being warehoused.
 The computing power is affordable.
 The competitive pressure is strong.
 Data mining tools have become available

RELATED FIELDS
 Data mining is an emerging multi-disciplinary field:
Statistics
Machine learning
Databases
Information retrieval
Visualization
etc.

DATA MINING (KDD) PROCESS
 Understand the application domain
 Identify data sources and select target data
 Pre-process: cleaning, attribute selection
 Data mining to extract patterns or models
 Post-process: identifying interesting or useful patterns
 Incorporate patterns in real world tasks

DATA MINING APPLICATIONS
 Marketing, customer profiling and retention,
identifying potential customers, market
segmentation.
 Fraud detection
identifying credit card fraud, intrusion detection
 Scientific data analysis
 Text and web mining
 Any application that involves a large amount of data.

WEB DATA EXTRACTION
Data
region1
Data
region2
A data
record
A data
record

OPINION ANALYSIS
 Word-of-mouth on the Web
 The Web has dramatically changed the way that
consumers express their opinions.
 One can post reviews of products at merchant
sites, Web forums, discussion groups, blogs
 Techniques are being developed to exploit these
sources.
 Benefits of Review Analysis
 Potential Customer: No need to read many reviews
 Product manufacturer: market intelligence, product
benchmarking

FEATURE BASED ANALYSIS &
SUMMARIZATION
 Extracting product features (called Opinion Features) that
have been commented on by customers.
 Identifying opinion sentences in each review and
deciding whether each opinion sentence is positive or
negative.
 Summarizing and comparing results.

A Happy and Prosperous day to all friends.
This PPT presented By – Pawneshwar Datt Rai
ThisPPTpresentedBy-PawneshwarDattRai

Introduction to data mining technique

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