Open Source Data Mining Workshop
on Frequent Pattern Mining Implementations

in conjunction with ACM SIGKDD 2005
8.45 Opening
9.00 B. Racz, F. Bodon, L. Schmidt-Thieme:
On Benchmarking Frequent Itemset Mining Algorithms: from Measurement to Analysis
9.30 C. Borgelt:
Keeping Things Simple: Finding Frequent Item Sets by Recursive Elimination
9.45 T. Uno, M. Kiyomi, H. Arimura:
LCM ver 3.: Collaboration of Array, Bitmap and Prefix Tree for Frequent Itemset Mining
10.00 Coffee break
10.30 C. Borgelt, T. Meinl, M. Berthold:
MOSS: A Program for Molecular Substructure Mining
11.00 C.I. Ezeife, Y. Lu, Y. Liu:
PLWAP Sequential Mining: Open Source Code
11.30 N.S. Ketkar, L.B. Holder, D.J. Cook:
Subdue: Compression-Based Frequent Pattern Discovery in Graph Data
11.45 F. Bodon:
A Trie-based APRIORI Implementation for Mining Frequent Item Sequences
12.00 Lunch
13.30 Keynote talk: Geoff Webb
Finding the Real Patterns
14.30 B. Sayrafi, D. Van Gucht, P.W. Purdom:
On the Effectiveness and Efficiency of Computing Bounds on the Support of Item Sets in the Frequent Item Set Mining Problem
15.00 Coffee break
15.30 C. Borgelt:
An Implementation of the FP-growth Algorithm
16.00 M. ElHajj, O.R. Zaiane:
Implementing Leap Traversals of the Itemset Lattice
16.30 Closing
17.00 KDD Opening
Webmaster: Bart Goethals