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香港理工大学：数据仓库和数据挖掘（PPT讲稿）Data Warehousing & Data Mining
• Data Mining – From data warehousing to data mining. – Data pre-processing and data mining life-cycle. – Association and sequence analysis; classification and clustering. – Fuzzy Logic, Neural Networks, and Genetic Algorithms. – Mining Complex Data. • OLAP mining; spatial data mining; text mining; time-series data mining; web mining; visual data mining. • Data warehousing. – Introduction; basic concepts of data warehousing; data warehouse vs. Operational DB; data warehouse and the industry. – Architecture and design; two-tier and threetier architecture; star schema and snowflake schema; data capturing, replication, transformation and cleansing. – Data characteristics; metadata; static and dynamic data; derived data. – Data Marts; OLAP; data mining; data warehouse administration.
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Data Mining Vs. Statistical Inference 3) Therapy First 5000 patient 10% 25% 口非藥物 口三九顆粒劑 口中草藥 43% 22% 囗農本方 Therapy: Last 5000 patients 10% 25% 口非藥物 口三九顆粒劑 35% 口中草藥 30% 口農本方
19 Therapy: First 5000 patients 25% 22% 43% 10% 非藥物 三九顆粒劑 中草藥 農本方 Therapy: Last 5000 patients 25% 30% 35% 10% 非藥物 三九顆粒劑 中草藥 農本方 Data Mining vs. Statistical Inference (3)
Data Mining vs Linear Regression Y Yi=Bo +BiXi+e, 20
20 Data Mining vs. Linear Regression
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