• Publications
  • Influence
Mining molecular fragments: finding relevant substructures of molecules
TLDR
An algorithm to find fragments in a set of molecules that help to discriminate between different classes of for instance, activity in a drug discovery context is presented, which results in substantially faster search by eliminating the need for frequent, computationally expensive reembeddings and by suppressing redundant search. Expand
EFFICIENT IMPLEMENTATIONS OF APRIORI AND ECLAT
TLDR
Implementations of Apriori and Eclat are described that use several optimizations to achieve maximum performance, w.r.t. both execution time and memory usage. Expand
An implementation of the FP-growth algorithm
TLDR
This paper describes a C implementation of the FP-growth algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of theFP- growth algorithm), and reports experimental results comparing this implementation with three other frequent item set mining algorithms I implemented. Expand
Induction of Association Rules: Apriori Implementation
TLDR
An implementation of the well-known apriori algorithm for the induction of association rules that is based on the concept of a prefix tree, which may be used in order to minimize the time needed to find the frequent itemsets as well as to reduce the amount of memory needed to store the counters. Expand
Frequent item set mining
  • C. Borgelt
  • Computer Science
  • Wiley Interdiscip. Rev. Data Min. Knowl. Discov.
  • 1 November 2012
TLDR
This paper provides an overview of the foundations of frequent item set mining, starting from a definition of the basic notions and the core task, and discusses how the search space is structured to avoid redundant search, how the output is reduced by confining it to closed or maximal item sets or generators. Expand
Computational Intelligence
Computational Intelligence: A Methodological Introduction
This clearly-structured, classroom-tested textbook/reference presents a methodical introduction to the field of CI. Providing an authoritative insight into all that is necessary for the successfulExpand
Support Computation for Mining Frequent Subgraphs in a Single Graph
TLDR
This paper proposes a definition that relies on the non-existence of equivalent ancestor embeddings in order to guarantee that the resulting support is anti-monotone and describes a method to compute the support defined in this way. Expand
An extension to possibilistic fuzzy cluster analysis
We explore an approach to possibilistic fuzzy clustering that avoids a severe drawback of the conventional approach, namely that the objective function is truly minimized only if all cluster centersExpand
Guide to Intelligent Data Analysis - How to Intelligently Make Sense of Real Data
TLDR
The Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems, and is essential reading for all professionals who face dataAnalysis problems. Expand
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