《Foundations and Advances in Data Mining》---Springer
                                     Contents

Part I Foundations of Data Mining
The Mathematics of Learning: Dealing
with Data
T. Poggio, S. Smale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Logical Regression Analysis: From Mathematical Formulas
to Linguistic Rules
H. Tsukimoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
A Feature/Attribute Theory for Association Mining
and Constructing the Complete Feature Set
Tsau Young Lin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
A New Theoretical Framework for K-Means-Type Clustering
J. Peng, Y. Xia. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
Part II Recent Advances in Data Mining
Clustering Via Decision Tree Construction
B. Liu, Y. Xia, P.S. Yu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
Incremental Mining on Association Rules
W.-G. Teng, M.-S. Chen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
Mining Association Rules from Tabular Data Guided
by Maximal Frequent Itemsets
Q. Zou, Y. Chen, W.W. Chu, X. Lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
X Contents
Sequential Pattern Mining
by Pattern-Growth: Principles
and Extensions
J. Han, J. Pei, X. Yan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Web Page Classification
B. Choi, Z. Yao . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221
Web Mining – Concepts, Applications
and Research Directions
J. Srivastava, P. Desikan, V. Kumar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275
Privacy-Preserving Data Mining
C. Clifton, M. Kantarcıoˇglu, J. Vaidya . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309