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. Data Mining Chapter 1 Data vs Information Society produces huge amounts of data Potentially valuable resource Needs to be organized patterns underlying data Data Mining People have always looked for patterns Computing power makes more feasible Data Mining Extraction of implicit previously unknown and potentially useful information from data Introduction Info. L1-2 Homework 4 distribution Chapter 9. L1-2 W13L1-2 Chapter 10.

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Data mining notes AVC College of Engineering. Chapter 1 Introduction to Data Mining Outline Motivation of Data Mining Concepts of Data Mining Applications of Data Mining Data Mining Functionalities Focus of Data. Mining complex types of data W12.

Advanced Frequent Pattern Mining. It supplements the discussions in the other chapters with a discussion of the statistical concepts statistical significance p-values false discovery rate permutation. Clustering analysis W10L2 W11.

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Spatial Data Mining 71 Pattern Discovery 72 Motivation 73 Classification Techniques 74 Association Rule Discovery Techniques 75 Clustering 76 Outlier Detection - Title. Data Warehousing and On-Line Analytical Processing. Remove them for more efficient operations with Celonis EMS.

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A completely new addition in the second edition is a chapter on how to avoid false discoveries and produce valid results which is novel among other contemporary textbooks on data mining.

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