Mining multi-level association rules using data cubes and mining N-most interesting itemsets.

by Kwong, Wang-Wai Renfrew.Thesis (M.Phil.)--Chinese University of Hong Kong, 2000.Includes bibliographical references (leaves 102-105).Abstracts in English and Chinese.Abstract --- p.iiAcknowledgments --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Data Mining Tasks --- p.1Chapter 1.1.1 --- Characterization --- p.2Chapter 1.1.2 --- Discrimination --- p.2Chapter 1.1.3 --- Classification --- p.2Chapter 1.1.4 --- Clustering --- p.3Chapter 1.1.5 --- Prediction --- p.3Chapter 1.1.6 --- Description --- p.3Chapter 1.1.7 --- Association Rule Mining --- p.4Chapter 1.2 --- Motivation --- p.4Chapter 1.2.1 --- Motivation for Mining Multi-level Association Rules Using Data Cubes --- p.4Chapter 1.2.2 --- Motivation for Mining N-most Interesting Itemsets --- p.8Chapter 1.3 --- Outline of the Thesis --- p.10Chapter 2 --- Survey on Previous Work --- p.11Chapter 2.1 --- Data Warehousing --- p.11Chapter 2.1.1 --- Data Cube --- p.12Chapter 2.2 --- Data Mining --- p.13Chapter 2.2.1 --- Association Rules --- p.14Chapter 2.2.2 --- Multi-level Association Rules --- p.15Chapter 2.2.3 --- Multi-Dimensional Association Rules Using Data Cubes --- p.16Chapter 2.2.4 --- Apriori Algorithm --- p.19Chapter 3 --- Mining Multi-level Association Rules Using Data Cubes --- p.22Chapter 3.1 --- Use of Multi-level Concept --- p.22Chapter 3.1.1 --- Multi-level Concept --- p.22Chapter 3.1.2 --- Criteria of Using Multi-level Concept --- p.23Chapter 3.1.3 --- Use of Multi-level Concept in Association Rules --- p.24Chapter 3.2 --- Use of Data Cube --- p.25Chapter 3.2.1 --- Data Cube --- p.25Chapter 3.2.2 --- Mining Multi-level Association Rules Using Data Cubes --- p.26Chapter 3.2.3 --- Definition --- p.28Chapter 3.3 --- Method for Mining Multi-level Association Rules Using Data Cubes --- p.31Chapter 3.3.1 --- Algorithm --- p.33Chapter 3.3.2 --- Example --- p.35Chapter 3.4 --- Experiment --- p.44Chapter 3.4.1 --- Simulation of Data Cube by Array --- p.44Chapter 3.4.2 --- Simulation of Data Cube by B+ Tree --- p.48Chapter 3.5 --- Discussion --- p.54Chapter 4 --- Mining the N-most Interesting Itemsets --- p.56Chapter 4.1 --- Mining the N-most Interesting Itemsets --- p.56Chapter 4.1.1 --- Criteria of Mining the N-most Interesting itemsets --- p.56Chapter 4.1.2 --- Definition --- p.58Chapter 4.1.3 --- Property --- p.59Chapter 4.2 --- Method for Mining N-most Interesting Itemsets --- p.60Chapter 4.2.1 --- Algorithm --- p.60Chapter 4.2.2 --- Example --- p.76Chapter 4.3 --- Experiment --- p.81Chapter 4.3.1 --- Synthetic Data --- p.81Chapter 4.3.2 --- Real Data --- p.85Chapter 4.4 --- Discussion --- p.98Chapter 5 --- Conclusion --- p.100Bibliography --- p.101Appendix --- p.106Chapter A --- Programs for Mining the N-most Interesting Itemset --- p.106Chapter A.1 --- Programs --- p.106Chapter A.2 --- Data Structures --- p.108Chapter A.3 --- Global Variables --- p.109Chapter A.4 --- Functions --- p.110Chapter A.5 --- Result Format --- p.113Chapter B --- Programs for Mining the Multi-level Association Rules Using Data Cube --- p.114Chapter B.1 --- Programs --- p.114Chapter B.2 --- Data Structure --- p.118Chapter B.3 --- Variables --- p.118Chapter B.4 --- Functions --- p.11