Advances in Data Mining and Modeling: Hong Kong 27 - 28 June 2002

Data Mining......Page 6
Preface......Page 8
Author Index......Page 14
1. Introduction......Page 16
2. Problem Definition and Model......Page 18
3.1. GSP......Page 19
3.2. MFS......Page 21
3.3. Prefixspan......Page 22
3.4. SPADE......Page 24
References......Page 28
1. Introduction......Page 30
2. Introduction of Fisher Linear Discriminant......Page 31
3. Gene Selection Based on Fisher Optimization Model......Page 32
4.1. MIT AMWALL Data......Page 34
4.3. Discussion......Page 35
References......Page 36
1. Introduction......Page 40
2.1. The k-means algorithm......Page 42
2.2. The k-modes and k-prototypes algorithms......Page 44
2.3. The fuzzy versions of k-means type algorithms......Page 45
3. Visual Cluster Validation......Page 46
3.1. Fastmap algorithm......Page 47
3.2. Cluster Validation with Fastmap......Page 48
where d is a distance function.......Page 50
5. Conclusions......Page 52
References......Page 53
1. Introduction......Page 55
2. Unidimensional Scaling......Page 56
3. Cluster analysis using UDS......Page 59
4. Conclusion......Page 61
References......Page 62
1. INTRODUCTION......Page 63
2. A NEWS SENSITIVE STOCK TREND PREDICTION SYSTEM......Page 64
2.1. Stock Trend Discovery......Page 65
2.2. Stock Trend Labeling......Page 66
2.3. Article and Trend Alignment......Page 68
2.4. Diflerentiated Feature Weighting......Page 69
3.1. Trends Discovery and Labeling......Page 70
3.2. Overall System Performance......Page 71
4. Conclusions......Page 72
References......Page 73
1. Introduction......Page 75
1.1. Related Work......Page 76
2.1. Problem Statement......Page 77
2.2. Discovery of an Associated Network Structure......Page 80
3.1. Algorithm for Inference with Implication Networks......Page 84
3.2. Algorithm for Inference with Association Rules......Page 85
4. Empirical Validation on Financial Market Analysis......Page 86
Japanese Market......Page 92
4.1. The Validation Procedure......Page 87
4.2. Settings for the Validation Experiments......Page 88
4.3.1. The Discovered Network Structure......Page 93
4.3.2. Association Rules Connecting the Networks......Page 94
4.3.3. Experiment to Evaluate the Overall Analysis Performance......Page 95
4.4. Discussion......Page 96
5. Conclusion......Page 97
References......Page 98
1. Introduction......Page 100
2. Identifying Peaks and Troughs in a Price Chart......Page 102
3. Data and Methodology......Page 104
4. Empirical Results......Page 106
5. Conclusion......Page 109
References......Page 110
Data Modeling......Page 7
1. Introduction......Page 112
2.2. Learning Algorithm......Page 114
3.1. Experiment 1......Page 117
3.2. Experiment 2......Page 118
4. Concluding Remarks......Page 119
References......Page 120
1. Introduction......Page 122
2. Mathematical Models of Recurrent Neural Networks......Page 123
3. Methodology......Page 124
4. Learning Algorithm of Discrete-time Recurrent Networks......Page 126
5. Results and Discussions......Page 127
References......Page 129
1. Introduction......Page 131
2. A Theorem on the One-Bit-Matching Conjecture......Page 133
3.1. Brief Review on LPM-ICA Algorithm......Page 136
3.2. A Simplified LPM-ICA Algorithm......Page 137
4.1. On Synthetical Data......Page 139
References......Page 141
1. Introduction......Page 144
2. Higher-order Markov Chain Models......Page 146
3. Parameters Estimation......Page 147
3.1. Linear Programming Formulation for Estimation of   i......Page 149
3.2. An Example......Page 150
4. Some Practical Examples......Page 152
4.1. The DNA Sequence......Page 153
5. Concluding Remarks......Page 155
References......Page 156
1. Introduction......Page 157
2. The Models......Page 158
3. Sunspot Numbers......Page 161
4. Discussion......Page 165
References......Page 166
1. Introduction......Page 167
2.2. Yield Curve in the SSE......Page 168
2.3. Expectations Hypothesis and Testing......Page 169
3.1. Forecasting Bond Excess Returns with Term Structure......Page 171
3.3. Principal Component Analysis......Page 172
3.4. A Single Factor for Forecasting Bond Expected Returns......Page 173
3.4 Two factors for Forecasting Bond Expected Returns......Page 175
4.2. Yield Curve Shocks......Page 177
4.3. Empirical Results......Page 178
5. Concluding Remarks......Page 180
References......Page 181
1. Introduction......Page 182
2. Two Models for Repeat Buying......Page 185
3. Two Models for Repeat Buying......Page 186
4. Targeting Loyal Customers for Direct Marketing......Page 189
5. Conclusion......Page 194
References......Page 195