Information about Multilevel techniques for the clustering problem

Data Mining is concerned with the discovery of interesting patterns and knowledge in data

repositories. Cluster Analysis which belongs to the core methods of data mining is the process

of discovering homogeneous groups called clusters. Given a data-set and some measure of

similarity between data objects, the goal in most clustering algorithms is maximizing both the

homogeneity within each cluster and the heterogeneity between different clusters. In this work,

two multilevel algorithms for the clustering problem are introduced. The multilevel

paradigm suggests looking at the clustering problem as a hierarchical optimization process

going through different levels evolving from a coarse grain to fine grain strategy. The clustering

problem is solved by first reducing the problem level by level to a coarser problem where an

initial clustering is computed. The clustering of the coarser problem is mapped back level-bylevel

to obtain a better clustering of the original problem by refining the intermediate different

clustering obtained at various levels. A benchmark using a number of data sets collected from a

variety of domains is used to compare the effectiveness of the hierarchical approach against its

single-level counterpart.

repositories. Cluster Analysis which belongs to the core methods of data mining is the process

of discovering homogeneous groups called clusters. Given a data-set and some measure of

similarity between data objects, the goal in most clustering algorithms is maximizing both the

homogeneity within each cluster and the heterogeneity between different clusters. In this work,

two multilevel algorithms for the clustering problem are introduced. The multilevel

paradigm suggests looking at the clustering problem as a hierarchical optimization process

going through different levels evolving from a coarse grain to fine grain strategy. The clustering

problem is solved by first reducing the problem level by level to a coarser problem where an

initial clustering is computed. The clustering of the coarser problem is mapped back level-bylevel

to obtain a better clustering of the original problem by refining the intermediate different

clustering obtained at various levels. A benchmark using a number of data sets collected from a

variety of domains is used to compare the effectiveness of the hierarchical approach against its

single-level counterpart.

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Abstract. Data Mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster Analysis which belongs to the ...

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Data Mining is concerned with the discovery of interesting patterns and knowledge in data repositories. Cluster Analysis which belongs to the core methods ...

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Keywords: Clustering problem, genetic algorithm, multilevel paradigm, ... Section 2 presnets a short survey of techniques for the clustering problem.

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Both algorithms demonstrate that the multilevel technique is capable of aiding the ... refinement algorithms for the capacitated clustering problem.

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... for the capacitated clustering problem. Multilevel refinement is a collaborative technique capable of ... the multilevel technique is capable ...

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... we introduce a Multilevel clustering technique designed as a parallel algorithm and solely implemented on the ... for some clustering problems, ...

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Under what conditions should one use multilevel ... you may want to impose more "structure" on the problem. ... clustering or structure in the ...

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have been proposed for the graph clustering problem. We ... multilevel clustering algorithms ... a new multilevel algorithm for graph clustering.

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multi-level clustering techniques are more or less ... The application of multi-level clustering reduces the problem space to a level where LR-FM can ...

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