数据挖掘导论英文chap1_intro.ppt

(C) Vipin Kumar, CSci 5980 Data Mining, Spring 2004 (C) Vipin Kumar, Parallel Issues in Data Mining, VECPAR 2002 Data Mining: Introduction Why Mine Data? Commercial Viewpoint Lots of data is being collected and warehoused Web data, e-commerce purchases at department/grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong Provide better, customized services for an edge (e.g. in Customer Relationship Management) Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) remote sensors on a satellite telescopes scanning the skies microarrays generating gene expression data scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists in classifying and segmenting data in Hypothesis Formation Mining Large Data Sets - Motivation There is often information “hidden” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns  What is (not) Data Mining? Origins of Data Mining Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional Techniquesmay be unsuitable due to Enormity of data High dimensionality of data Heterogeneous, distributed nature of data Data Mining Tasks Prediction Methods Use some variables to predict unknown or future values of other variables. Description Methods Find human-interpretable patterns that describe the data. Data Mining Tasks... Classification [Predictive] Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Re