Intelligent Database Systems Lab

Presenter : Chuang, Kai-Ting

Authors : Guillaume Cleuziou*

2013, PRL

OSOM: A method for building overlapping topological maps

Intelligent Database Systems Lab

Outlines Motivation Objectives Methodology Experiments Conclusions Comments

Intelligent Database Systems Lab

Motivation• Overlapping clustering solutions extract data

organizations that are more fitted to the input data

than crisp clustering solutions.

• Unsupervised neural networks bring efficient

solutions to visualize class structures.

Intelligent Database Systems Lab

Objectives• We present the algorithm O-SOM that uses both an

overlapping variant of the k-means clustering

algorithm and the well known Kohonen approach, in

order to build overlapping topologic maps.

• To solve problems that are recurrent in overlapping

clustering: number of clusters, complexity of the

algorithm and coherence of the overlaps.

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Methodology-Framework

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Methodology

OSOM SOM

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Methodolog-fast-osom

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Experiment-dataset

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Experiment-evaluation framework

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Experiment results

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Experiment-Topological evaluation

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Conclusions

• Ensure the algorithm to converge and then bring

solutions to the motivations mentioned: limited

complexity, topological correctness, etc.

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Comments• Advantages– The OSOM is simple method.

• Applications– Topological maps.