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· DATA WAREHOUSING AND MINIG LECTURE NOTES Clustering high dimensional data. Clustering highdimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions. Such highdimensional data spaces are often encountered in areas such as medicine, where DNA microarray technology can produce a large ...
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Sub Code : CS2032 Sub Name: Data Warehousing and Data Mining UNITI PART A DATA WAREHOUSING 1. Define the term 'Data Warehouse'. A data warehouse is a subjectoriented, integrated, timevariant and non volatile collection of data in support of management's decision making process. SubjectOriented: A data warehouse can be used to analyze a particular subject area. For .
COURSE LECTURE NOTES COURSE OBJECTIVES (COs): The course should enable the students to: I Identifying necessity of Data Mining and Data Warehousing for the society. II Familiar with the process of data analysis, identifying the problems, and choosing the relevant models and algorithms to apply. III Develop skill in selecting the appropriate data mining algorithm for solving practical problems ...
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To Download CS2032 DATA WAREHOUSING AND DATA MINING NOTES [UNIT I and II] Click HERE. Data Warehouse Introduction. A data warehouse is a collection of data marts representing historical data from different operations in the company. This data is stored in a structure optimized for querying and data analysis as a data warehouse. Table design, dimensions and organization should be .
LECTURE NOTES ON DATA WAREHOUSE AND DATA MINING III B. Tech II semester (JNTUHR15) Mr. Ch Suresh Kumar Raju Assistant Professor INFORMATION TECHNOLOGY INSTITUTE OF AERONAUTICAL ENGINEERING (Autonomous) DUNDIGAL, HYDERABAD 500 043 . UNIT I What motivated data mining? Why is it important? The major reason that data mining has attracted a .
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CS2032 NOTES IN PDF cs2032 data warehouse and mining important question Although this may include characterization, discrimination, association and correlation analysis, classifiion, prediction, or clustering of time related data, distinct features of such an analysis include timeseries data analysis, sequence or periodicity pattern matching, and similarity ...