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Special Topics in Data Mining. Special Topics in Data Mining. Direct Objectives To learn data mining techniques To see their use in real-world/research applications To get an understanding of the methodological principles behind data mining

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special topics in data mining1

SpecialTopics in Data Mining

Direct Objectives

  • To learn data mining techniques
  • To see their use in real-world/research applications
  • To get an understanding of the methodological principles behind data mining
  • To be able to read about data mining in the popular press with a critical eye
  • To implement & use data mining models using DM software
special topics in data mining2

SpecialTopics in Data Mining

GradeStructure

Review Paper & Presentation : 30%

Final Project Implementation & Present. :40%

Final Project Paper : 30%

special topics in data mining3

Data Mining in Specific fieldforReview Paper

    • Data Mining in Security
    • Data Mining in Telecommunications and Control
    • Text and Web Mining
    • Data Mining in Biomedicine and Science
    • Data Mining for Insurance
    • Data Mining in Banking and Commercial
    • Data Mining in Sales Marketing and Finance
    • Data Mining in Business

SpecialTopics in Data Mining

what is data mining

Not well defined…. Since Data Mining is Confluence of Multiple Disciplines

No one can agree on what data mining is!

In fact the experts have very different descriptions:

Different fields have different views of what data mining is (also different terminology!)

What is Data Mining?

what is data mining1

Database

Technology

Statistics

Data Mining

Machine

Learning

Visualization

Information

Science

Other

Disciplines

Since Data Mining is Confluence of Multiple Disciplines

What is Data Mining?

what is data mining2

“finding interesting structure (patterns, statistical models, relationships) in data bases”. - Fayyad, Chaduriand

  • “the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data.” - Fayyad

What is Data Mining?

what is data mining3

What is Data Mining?

  • “a knowledge discovery process of extracting previously unknown, actionable information from very large data bases” – Zorne
  • “a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions.”--- Edelstein
what is data mining4

Data mining is the process of extracting hidden patterns from data.

  • Data mining is the process of discovering new patterns from large data sets involving methods from statistics and artificial intelligence but also database management.
  • “data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” Hand, Mannila, Smyth

What is Data Mining?

what is data mining5

Knowledge Discovery in Databases (KDD)

  • Data Mining, also popularly known as Knowledge Discovery in Databases (KDD)...
  • The Knowledge Discovery in Databases process comprises of a few steps leading from raw data collections to some form of new knowledge. The iterative process consists of the following steps: (From Zaiane)
    • Data cleaning: ...
    • Data integration: ...
    • Data selection: ...
    • Data transformation: ...
    • Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful.
    • Pattern evaluation: ...
    • Knowledge representation: ...

What is Data Mining?

what is data mining6

Knowledge Discovery in Databases (KDD)

  • …..
    • Data mining: it is the crucial step in which clever techniques are applied to extract patterns potentially useful.
    • …..

What is Data Mining?

what is data mining7

What is Data Mining?

  • Software
  • Can use any software you like – must know how to input, manipulate, graph, and analyze data.
  • SAS, Weka, SPSS, Systat, Enterprise Miner, JMP, Minitab, Matlab, SQL Server
what is data mining8

What is Data Mining?

  • Software
  • Can use any software you like – must know how to input, manipulate, graph, and analyze data.
  • SAS, Weka, SPSS, Systat, Enterprise Miner, JMP, Minitab, Matlab, SQL Server
data data data
Data DataData
  • It’s all about the data - where does it come from?
    • www
    • Gene
    • Business processes/transactions
    • Telecommunications and networking
    • Medical imagery
    • Government, demographics (data.gov!)
    • Sensor networks
    • sports
what is data
What is Data?
  • Collection of objects and their attributes
  • An attribute is a property or characteristic of an object
    • Examples: eye color of a person, temperature, etc.
    • Attribute is also known as variable, field, characteristic, or feature
  • A collection of attributes describe an object
    • Object is also known as record, point, case, sample, entity, or instance
    • Attribute values are numbers or symbols assigned to an attribute

Attributes

Objects

record data
Record Data
  • Data that consists of a collection of records, each of which consists of a fixed set of attributes
document data
Document Data
  • Each document becomes a `term' vector,
    • each term is a component (attribute) of the vector,
    • the value of each component is the number of times the corresponding term occurs in the document.
transaction data
Transaction Data
  • A special type of record data, where
    • each record (transaction) involves a set of items.
    • For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.
transaction data1
Transaction Data

weblogs, phone calls…

128.195.36.195, -, 3/22/00, 10:35:11, W3SVC, SRVR1, 128.200.39.181, 781, 363, 875, 200, 0, GET, /top.html, -,

128.195.36.195, -, 3/22/00, 10:35:16, W3SVC, SRVR1, 128.200.39.181, 5288, 524, 414, 200, 0, POST, /spt/main.html, -,

128.195.36.195, -, 3/22/00, 10:35:17, W3SVC, SRVR1, 128.200.39.181, 30, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.195.36.101, -, 3/22/00, 16:18:50, W3SVC, SRVR1, 128.200.39.181, 60, 425, 72, 304, 0, GET, /top.html, -,

128.195.36.101, -, 3/22/00, 16:18:58, W3SVC, SRVR1, 128.200.39.181, 8322, 527, 414, 200, 0, POST, /spt/main.html, -,

128.195.36.101, -, 3/22/00, 16:18:59, W3SVC, SRVR1, 128.200.39.181, 0, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:54:37, W3SVC, SRVR1, 128.200.39.181, 140, 199, 875, 200, 0, GET, /top.html, -,

128.200.39.17, -, 3/22/00, 20:54:55, W3SVC, SRVR1, 128.200.39.181, 17766, 365, 414, 200, 0, POST, /spt/main.html, -,

128.200.39.17, -, 3/22/00, 20:54:55, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:55:07, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 1061, 382, 414, 200, 0, POST, /spt/main.html, -,

128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:55:39, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:56:03, W3SVC, SRVR1, 128.200.39.181, 1081, 382, 414, 200, 0, POST, /spt/main.html, -,

128.200.39.17, -, 3/22/00, 20:56:04, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:56:33, W3SVC, SRVR1, 128.200.39.181, 0, 262, 72, 304, 0, GET, /top.html, -,

128.200.39.17, -, 3/22/00, 20:56:52, W3SVC, SRVR1, 128.200.39.181, 19598, 382, 414, 200, 0, POST, /spt/main.html, -,

graph data
Graph Data
  • Examples: Generic graph and HTML Links
ordered data
Ordered Data
  • Genomic sequence data
spatio temporal data
Spatio-Temporal Data

Average Monthly Temperature of land and ocean

slide24

Relational Data

128.200.39.17, -, 3/22/00, 20:55:07, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 1061, 382, 414, 200, 0, POST, /spt/main.html, -,

128.200.39.17, -, 3/22/00, 20:55:36, W3SVC, SRVR1, 128.200.39.181, 0, 258, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

128.195.36.195, -, 3/22/00, 10:35:11, W3SVC, SRVR1, 128.200.39.181, 781, 363, 875, 200, 0, GET, /top.html, -,

128.195.36.195, -, 3/22/00, 10:35:16, W3SVC, SRVR1, 128.200.39.181, 5288, 524, 414, 200, 0, POST, /spt/main.html, -,

128.195.36.195, -, 3/22/00, 10:35:17, W3SVC, SRVR1, 128.200.39.181, 30, 280, 111, 404, 3, GET, /spt/images/bk1.jpg, -,

…,

128.195.36.195, Doe, John, 12 Main St, 973-462-3421, Madison, NJ, 07932

114.12.12.25,Trank, Jill, 11 Elm St, 998-555-5675, Chester, NJ, 07911

07911, Chester, NJ, 07954, 34000, , 40.65, -74.12

07932, Madison, NJ, 56000, 40.642, -74.132

  • Most large data sets are stored in relational data sets
  • Special data query language: SQL
  • Oracle, MSFT, IBM
  • Good open source versions: MySQL, PostGres
data quality
Data Quality
  • What kinds of data quality problems?
  • How can we detect problems with the data?
  • What can we do about these problems?
  • Examples of data quality problems:
    • Noise and outliers
    • missing values
    • duplicate data
noise
Noise
  • Noise refers to modification of original values
    • Examples: distortion of a person’s voice when talking on a poor phone and “snow” on television screen

Two Sine Waves

Two Sine Waves + Noise

outliers
Outliers
  • Outliers are data objects with characteristics that are considerably different than most of the other data objects in the data set
missing values
Missing Values
  • Reasons for missing values
    • Information is not collected (e.g., people decline to give their age and weight)
    • Attributes may not be applicable to all cases (e.g., annual income is not applicable to children)
  • Handling missing values
    • Eliminate Data Objects
    • Estimate Missing Values
    • Ignore the Missing Value During Analysis
    • Replace with all possible values (weighted by their probabilities)
duplicate data
Duplicate Data
  • Data set may include data objects that are duplicates, or almost duplicates of one another
    • Major issue when merging data from heterogeous sources
  • Examples:
    • Same person with multiple email addresses
  • Data cleaning
    • Process of dealing with duplicate data issues
examples of data mining successes
Examples of Data Mining Successes
  • Market Basket (WalMart)
  • Recommender Systems (Amazon.com)
  • Fraud Detection in Telecommunications (AT&T)
  • Target Marketing / CRM
  • Financial Markets
  • DNA Microarray analysis
  • Web Traffic / Blog analysis