DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn

Data Mining Outline – Introduction – Related Concepts – Data Mining Techniques © Prentice

Introduction Outline Goal: Provide an overview of data mining. Define data mining n Data

Introduction n Data is growing at a phenomenal rate (read “How Much Information Is

Data Mining Definition Finding hidden information in a database n Data Mining has been

Database Processing vs. Data Mining Processing n Query n – Poorly defined – No

Query Examples n Database – Find all credit applicants with last name of Smith.

Data Mining Models and Tasks © Prentice Hall 8

Basic Data Mining Tasks I n Classification maps data into predefined groups or classes

Basic Data Mining Tasks II n Summarization maps data into subsets with associated simple

KDD Process Modified from [FPSS 96 C] n n n Selection: Obtain data from

KDD Process Ex: Shuttle Data n Selection: – Select data (which missions etc) to

Data Mining Development • Relational Data Model • SQL • Association Rule Algorithms •

KDD Issues Human Interaction n Overfitting n Outliers n Interpretation n Visualization n Large

KDD Issues (cont’d) Multimedia Data n Missing Data n Irrelevant Data n Noisy Data

Social Implications of DM Privacy n Profiling n Unauthorized use n © Prentice Hall

Data Mining Metrics Usefulness n Return on Investment (ROI) n Accuracy n Space/Time Complexity

Slides: 17

Download presentation

DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M. H. Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002. http: //iubio. indiana. edu/treeapp/treeprint-sample 1. html © Prentice Hall 1

Data Mining Outline – Introduction – Related Concepts – Data Mining Techniques © Prentice Hall 2

Introduction Outline Goal: Provide an overview of data mining. Define data mining n Data mining vs. databases n Basic data mining tasks n Data mining issues n © Prentice Hall 3

Introduction n Data is growing at a phenomenal rate (read “How Much Information Is There In the World? ” By Michael Lesk ) Users expect more sophisticated information How? UNCOVER HIDDEN INFORMATION DATA MINING © Prentice Hall 4

Data Mining Definition Finding hidden information in a database n Data Mining has been defined as “The nontrivial extraction of implicit, previously unknown, and potentially useful information from data”. n Similar terms n – Exploratory data analysis – Data driven discovery – Deductive learning – Discovery Science – Knowledge Discovery © Prentice Hall 5

Database Processing vs. Data Mining Processing n Query n – Poorly defined – No precise query language – Well defined – SQL n Query Output n – Subset of database Output –Not a subset of database © Prentice Hall 6

Query Examples n Database – Find all credit applicants with last name of Smith. – Identify customers who have purchased more than $10, 000 in the last month. – Find all customers who have purchased milk n Data Mining – Find all credit applicants who are poor credit risks. (classification) – Identify customers with similar buying habits. (Clustering) – Find all items which are frequently purchased with milk. (association rules) © Prentice Hall 7

Data Mining Models and Tasks © Prentice Hall 8

Basic Data Mining Tasks I n Classification maps data into predefined groups or classes – Supervised learning – Pattern recognition – Prediction n n Regression is used to map a data item to a real valued prediction variable. Clustering groups similar data together into clusters. – Unsupervised learning – Segmentation – Partitioning © Prentice Hall H =1. 31 (Fem + Fib) + 63. 05 9

Basic Data Mining Tasks II n Summarization maps data into subsets with associated simple descriptions. – Characterization – Generalization n Link Analysis uncovers relationships among data. – – – Affinity Analysis Association Rules Sequential Analysis determines sequential patterns. © Prentice Hall 10

KDD Process Modified from [FPSS 96 C] n n n Selection: Obtain data from various sources. Preprocessing: Cleanse data. Transformation: Convert to common format. Transform to new format. Data Mining: Obtain desired results. Interpretation/Evaluation: Present results to user in meaningful manner. © Prentice Hall 11

KDD Process Ex: Shuttle Data n Selection: – Select data (which missions etc) to use n Preprocessing: – Remove Spikes n Transformation: 1 0. 9 0. 8 0. 7 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 00 100 200 300 400 500 600 700 800 900 1000 1 0. 9 0. 8 0. 7 0. 6 0. 5 0. 4 0. 3 0. 2 0. 1 0 – DFT, DWT, PAA etc n Data Mining: – Look for Rules… n Interpretation/Evaluation: 0 100 200 300 400 500 600 700 800 900 1000 – Show rules to domain experts n Potential User Applications: – Prediction of Failures© Prentice Hall 12

Data Mining Development • Relational Data Model • SQL • Association Rule Algorithms • Data Warehousing • Scalability Techniques • Similarity Measures • Hierarchical Clustering • IR Systems • Imprecise Queries • Textual Data • Web Search Engines • Bayes Theorem • Regression Analysis • EM Algorithm • K-Means Clustering • Time Series Analysis • Algorithm Design Techniques • Algorithm Analysis • Data Structures • Neural Networks • Decision Tree Algorithms © Prentice Hall 13

KDD Issues Human Interaction n Overfitting n Outliers n Interpretation n Visualization n Large Datasets n High Dimensionality n © Prentice Hall 14

KDD Issues (cont’d) Multimedia Data n Missing Data n Irrelevant Data n Noisy Data n Changing Data (streams) n Integration n Application n © Prentice Hall 15