Practical Applications of Data Mining

Index

A

agglomerative clustering

algorithm, 291, 292–293

alpha-cut threshold, 379–383, 384

alternative hypothesis, 144–145

analysis of variance (ANOVA), 167–169

approximation space, 186, 187

Apriori algorithm, 30–33, 70

apriori-gen( ) function, 32

AprioriTid algorithm, 33–35

association rules, 7–8, 25–26,

79, 289–290

for basket data, 29–35

Boolean, 53

clustering on, 290, 313–329

compact, 59–66, 70

composite, 47–52

distance-based, 326–327

for hypertext systems, 46–52, 67, 70

quantitative, 53–58, 70

for relational data, 35–46, 59–66

with timing information, 67–70

attribute-oriented rule induction, 35–46

attribute value system, 188

B

b -approximation space, 187

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