presenter : chuang, kai-ting authors : guillaume cleuziou* 2013, prl

Intelligent Database Systems Lab

Presenter : Chuang, Kai-Ting

Authors : Guillaume Cleuziou*

2013, PRL

OSOM: A method for building overlapping topological maps


Outlines Motivation Objectives Methodology Experiments Conclusions Comments


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.


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.


Methodology-Framework


Methodology

OSOM SOM


Methodolog-fast-osom


Experiment-dataset


Experiment-evaluation framework


Experiment results


Experiment-Topological evaluation


Conclusions

• Ensure the algorithm to converge and then bring

solutions to the motivations mentioned: limited

complexity, topological correctness, etc.


Comments• Advantages– The OSOM is simple method.

• Applications– Topological maps.

presenter : chuang, kai-ting authors : guillaume cleuziou* 2013, prl

Documents