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2 Ziembiński R. Z.

2 2011

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Bclusge: application of the law of buoyancy in the luster analysis

Ziembiński R. Z.

Foundations of Computing and Decision Sciences

2011

Vol. 36, No. 2

131-155

The paper introduces a new algorithm for a cluster analysis named BClusGe. It uses a technique of the subspace clustering with an empty space removal. This new algorithm produces a multidimensional rectangular grid tightly adjusted to the processed data distribution. Afterwards, BClusGe merges adjacent folds contain-ing objects and produce the output set of clusters. An execution time of BClusGe depends on a number of folds created at the partitioning to describe the data distri-bution. Thus, data with the irregular distribution are clustered slower. Additionally, BClusGe delivers a B-tree structure describing found clusters. Its leafs are interconnected and allow to navigate between dense subspaces of the data distribution. The tree is useful in practical applications because it organizes hierarchically dependencies between clusters. The paper contains some experiments' results where BClusGe performance was compared to DBScan and K-means.

Accuracy of generalized context patterns in the context based sequential patterns mining

Ziembiński R. Z.

Control and Cybernetics

2011

Vol. 40, no 3

585-603

A context pattern is a frequent subsequence mined from the context database containing set of sequences. This kind of sequential patterns and all elements inside them are described by additional sets of context attributes e.g. continuous ones. The contexts describe circumstances of transactions and sources of sequential data. These patterns can be mined by an algorithm for the context based sequential pattern mining. However, this can create large sets of patterns because all contexts related to patterns are taken from the database. The goal of the generalization method is to reduce the context pattern set by introducing a more compact and descriptive kind of patterns. This is achieved by finding clusters of similar context patterns in the mined set and transforming them to a smaller set of generalized context patterns. This process has to retain as much as possible information from the mined context patterns. This paper introduces a definition of the generalized context pattern and the related algorithm. Results from the generalization may differ as depending on the algorithm design and settings. Hence, generalized patterns may reflect frequent information from the context database differently. Thus, an accuracy measure is also proposed to evaluate the generalized patterns. This measure is used in the experiments presented. The generalized context patterns are compared to patterns mined by the basic sequential patterns mining with prediscretization of context values.