Overview of Data Mining&

The Knowledge Discovery Process

Bamshad MobasherDePaul University

Bamshad MobasherDePaul University

Why Data Mining?

i The Explosive Growth of Data: from terabytes to petabytes4 Data collection and data availability

h Automated data collection tools, database systems, Web, computerized society

4 Major sources of abundant datah Business: Web, e-commerce, transactions, stocks, … h Science: Remote sensing, bioinformatics, scientific simulation, … h Society and everyone: news, images, video, documents, ….

2

3Source: Intel, 2012

4

From Data to Wisdom

i Data4 The raw material of

information

i Information4 Data organized and

presented by someone

i Knowledge4 Information read, heard or

seen and understood and integrated

i Wisdom4 Distilled knowledge and

understanding which can lead to decisions

Wisdom

Knowledge

Information

Data

The Information Hierarchy

5

What is Data Miningi What do we need?

4 Extract interesting and useful knowledge from the data4 Find rules, regularities, irregularities, patterns, constraints4 hopefully, this will help us better compete in business, do research, learn

concepts, make money, etc.

The non-trivial extraction of implicit, previously unknown and potentially useful knowledge from data in large data repositories

The non-trivial extraction of implicit, previously unknown and potentially useful knowledge from data in large data repositories

i Data Mining: A Definition

4 Non-trivial: obvious knowledge is not useful4 implicit: hidden difficult to observe knowledge4 previously unknown4 potentially useful: actionable; easy to understand

6

The Knowledge Discovery Processi Data Mining v. Knowledge Discovery in Data (KDD)

4 DM and KDD are often used interchangeably4 actually, DM is only part of the KDD process

- The KDD Process

7

Types of Knowledge Discoveryi Two kinds of knowledge discovery: directed and undirected

i Directed Knowledge Discovery4 Purpose: Explain value of some field in terms of all the others (goal-oriented)4 Method: select the target field based on some hypothesis about the data; ask the

algorithm to tell us how to predict or classify new instances4 Examples:

h what products show increased sale when cream cheese is discountedh which banner ad to use on a web page for a given user coming to the site

i Undirected Knowledge Discovery4 Purpose: Find patterns in the data that may be interesting (no target field)4 Method: clustering, affinity grouping4 Examples:

h which products in the catalog often sell togetherh market segmentation (find groups of customers/users with similar

characteristics or behavioral patterns)

From Data Mining to Data Science

8

9

Data Mining: On What Kinds of Data?

i Database-oriented data sets and applications

4 Relational database, data warehouse, transactional database

4 Object-relational databases, Heterogeneous databases and legacy databases

i Advanced data sets and advanced applications

4 Data streams and sensor data

4 Time-series data, temporal data, sequence data (incl. bio-sequences)

4 Structure data, graphs, social networks and information networks

4 Spatial data and spatiotemporal data

4 Multimedia database

4 Text data and other semi-structured data

4 The World-Wide Web

10

Data Mining: What Kind of Data?i Structured Databases

4 relational, object-relational, etc.4 can use SQL to perform parts of the process

e.g., SELECT count(*) FROM Items WHERE

type=video GROUP BY category

11

Data Mining: What Kind of Data?i Flat Files

4 most common data source4 can be text (or HTML) or binary4 may contain transactions, statistical data, measurements, etc.

i Transactional databases4 set of records each with a transaction id, time stamp, and a set of items4 may have an associated “description” file for the items4 typical source of data used in market basket analysis

12

Data Mining: What Kind of Data?i Other Types of Databases

4 legacy databases4 multimedia databases (usually very high-dimensional)4 spatial databases (containing geographical information, such as maps, or

satellite imaging data, etc.)4 Time Series Temporal Data (time dependent information such as stock market

data; usually very dynamic)

i World Wide Web4 basically a large, heterogeneous, distributed database4 need for new or additional tools and techniques

h information retrieval, filtering and extractionh agents to assist in browsing and filteringh Web content, usage, and structure (linkage) mining tools

4 The “social Web”h User generated meta-data, social networks, shared resources, etc.

13

What Can Data Mining Doi Many Data Mining Tasks

4 often inter-related4 often need to try different techniques/algorithms for each task4 each tasks may require different types of knowledge discovery

i What are some of data mining tasks4 Classification4 Prediction4 Clustering4 Affinity Grouping / Association discovery4 Sequence Analysis4 Characterization4 Discrimination

14

Some Applications of Data miningi Business data analysis and decision support

4 Marketing focalizationh Recognizing specific market segments that respond to particular

characteristicsh Return on mailing campaign (target marketing)

4 Customer Profilingh Segmentation of customer for marketing strategies and/or product

offeringsh Customer behavior understandingh Customer retention and loyaltyh Mass customization / personalization

15

Some Applications of Data miningi Business data analysis and decision support (cont.)

4 Market analysis and managementh Provide summary information for decision-makingh Market basket analysis, cross selling, market segmentation.h Resource planning

4 Risk analysis and managementh "What if" analysish Forecastingh Pricing analysis, competitive analysish Time-series analysis (Ex. stock market)

16

Some Applications of Data miningi Fraud detection

4 Detecting telephone fraud:h Telephone call model: destination of the call, duration, time of day or weekh Analyze patterns that deviate from an expected normh British Telecom identified discrete groups of callers with frequent intra-group calls,

especially mobile phones, and broke a multimillion dollar fraud scheme

4 Detection of credit-card fraud4 Detecting suspicious money transactions (money laundering)

i Text mining:4 Message filtering (e-mail, newsgroups, etc.)4 Newspaper articles analysis4 Text and document categorization

i Web Mining4 Mining patterns from the content, usage, and structure of Web resources