DataMining-SouthernMethodistUniversity-SMU.ppt

11/19/01 ? Prentice Hall DATA MININGIntroductory and Advanced TopicsPart I Margaret H. Dunham Department of Computer Science and Engineering Southern Methodist University Companion slides for the text by Dr. M.H.Dunham, Data Mining, Introductory and Advanced Topics, Prentice Hall, 2002. Data Mining Outline PART I Introduction Related Concepts Data Mining Techniques PART II Classification Clustering Association Rules PART III Web Mining Spatial Mining Temporal Mining Introduction Outline Define data mining Data mining vs. databases Basic data mining tasks Data mining development Data mining issues Introduction Data is growing at a phenomenal rate Users expect more sophisticated information How? Data Mining Definition Finding hidden information in a database Fit data to a model Similar terms Exploratory data analysis Data driven discovery Deductive learning Data Mining Algorithm Objective: Fit Data to a Model Descriptive Predictive Preference – Technique to choose the best model Search – Technique to search the data “Query” Database Processing vs. Data Mining Processing Query Well defined SQL Query Poorly defined No precise query language Query Examples Database Data Mining Data Mining Models and Tasks Basic Data Mining Tasks Classification maps data into predefined groups or classes Supervised learning Pattern recognition Prediction 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 Basic Data Mining Tasks (cont’d) Summarization maps data into subsets with associated simple descriptions. Characterization Generalization Link Analysis uncovers relationships among data. Affinity Analysis Association Rules Sequential Analysis determines sequential patterns. Ex: Time Series Analysis Example: Stock Market Predict future values Determine similar patterns over time Classify behavior Data Mining vs. KDD Knowledge Discovery in