Data mining is considered as one of the most powerful technologies that participates greatly in helping companies in any industry to focus on the most important information in their data warehouses. In this book, we developed a new approach that measures the effectiveness of data mining in helping retail websites designers to improve the structure of their websites during the design phase. This approach overcomes web usage mining drawbacks, improves the website design structure, and reduces maintenance efforts needed in the future.We studied the behavior of items with respect to time. We showed how association rule mining can be invested to improve the process of decision making in a retail business through exploring current and previous product buying behavior and predicting and controlling future trends and behaviors. Based on our idea that interesting frequent itemsets are mainly covered by many recent transactions, a new method to mine for interesting frequent itemsets is introduced. Finally, we introduced the TARtool which is a temporal dataset generator and an association rule miner.
Author(s): Asem Omari
Publisher: VDM Verlag
Year: 2008
Language: English
Commentary: 50468
Pages: 151
Motivation......Page 16
Contributions......Page 20
Dissertation Organization......Page 22
Knowledge Discovery......Page 24
The Knowledge Discovery Process......Page 25
Data Mining......Page 26
What Kind of Data Can be Mined......Page 27
Data Mining Methods......Page 30
Neural Networks......Page 31
Decision Trees......Page 32
Data Mining Tasks......Page 33
Clustering......Page 34
Classification......Page 36
Association Rule Mining......Page 37
Sequential Pattern Mining......Page 40
Data Mining Applications......Page 41
Software Engineering......Page 44
Software Engineering Process......Page 45
Web Engineering......Page 47
E-Commerce and Retail websites......Page 50
Data Mining in the Website Maintenance Phase (Related Work)......Page 53
Web Usage Mining......Page 54
Web Log File......Page 55
Clustering......Page 56
Association Rule Mining......Page 57
Data Cleaning......Page 58
User Identification......Page 59
Web Usage Mining for Adaptive Websites......Page 60
Improving Website Usability and Organization......Page 61
Adaptive Link......Page 62
Adaptive E-Commerce......Page 63
Web Usage Mining for Personalized Websites......Page 64
Web Content Mining......Page 67
Web Structure Mining......Page 68
Discussion......Page 69
Data Mining in the Website Design Phase......Page 72
Association Rule Mining During the Design Phase......Page 74
Experimental Work......Page 77
Method Evaluation......Page 81
Classification and Clustering During the Design Phase......Page 83
Experimental work......Page 85
Method Evaluation......Page 90
Datasets Availability......Page 93
Related Work......Page 95
Periodical Association Rule Mining......Page 96
Temporal Frequent Itemset Mining......Page 100
Experimental Work......Page 105
Application Fields......Page 108
Synthetic Temporal Dataset Generation......Page 110
Temporal Dataset Generation......Page 111
Datasets for Association Rule Mining......Page 112
Real World Versus Synthetic Datasets......Page 113
Dataset Generators and Software Solutions......Page 114
An E-Commerce Generator......Page 115
The ARtool Generator......Page 116
Enhancements for ARtool......Page 118
Time Stamp Generation......Page 120
Evaluation......Page 124
Summary......Page 129
Future Work......Page 131
References......Page 132
