A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence Yue Ming NJIT#: 31351707

Outline Background Introduction about K-means K-MWO Algorithm Clustering ensemble method Simulation and conclusion

Clustering   Courses on data mining or machine learning will usually start with clustering, because it is both simple and useful. It is an important part of a somewhat wider area of Unsupervised Learning, where the data we want to describe is not labeled. Difference with classification There is no label

Clustering   Given a set of data points, group them into a clusters so that: Points within each cluster are similar to each other Points from different clusters are dissimilar

K-Means algorithm An iterative clustering algorithm  Effective, widely used, all-around clustering algorithm An iterative clustering algorithm Aim find local maxima in each iteration. 

Mussels Wandering Optimization Algorithm K-Means algorithm Drawbacks it is very sensitive to its initial value as different initial values may lead to different solutions (2) it is based on an objective function simply and usually solves the extreme value problem by the gradient method So… Mussels Wandering Optimization Algorithm

A New Algorithm   Based on K-means and mussels wandering optimization (MWO) K-MWO

Mussels Wandering Optimization (MWO)   Based on swarm intelligence The population of mussels consists of N individuals, these individuals are in a certain spatial region of marine bed called the habitat. Mapped to a d-dimensional space Sd Each mussel has position.

Mussels Wandering Optimization (MWO)   MWO performs well and provides a new approach for solving complex optimization problems. Hence, by combining MWO with classic K-means, this work proposes K-MWO as a new clustering method based on swarm intelligence.

K-MWO  Each mussel

K-MWO 

K-MWO 

K-MWO 

BUT…   Although many clustering algorithms are efficient in dealing with specific problems, every one of them has its own limitations. They may produce different results on the same dataset. No clustering algorithm is applicable to various structures and different types of datasets. Clustering ensemble (CE) is recognized to be an important way to address the above problems.

Clustering ensemble （CE） CE uses an ensemble technology to produce a new clustering result by integrating several clustering results obtained from different clustering methods or the same method with different parameters. Advantages: it can improve the quality of clustering results, cluster a dataset with a categorical attribute, and detect and handle isolated points and noises. It can also deal with distributed data sources and process the data in parallel. This paper presents a new similarity-based clustering method based on weight information.

Clustering ensemble method   Similarity-based clustering ensemble Key idea: The weights for data points change in every iteration

Clustering ensemble method   Key idea: Based on a similarity-based method, by adding weight information.

Simulation & conclusion

Conclusion This work intends to test the clustering performance of K-MWO, and compare it with K-means and K-PSO to prove its effectiveness at first. The paper choose three clustering methods as the basic input ones to perform the proposed CE method WSCE.

Conclusion

Conclusion

Conclusion

Conclusion This work has proposed a new clustering algorithm called K- MWO, clustering method. It makes full use of the global optimization ability of MWO and local search ability of K-means. This work has also proposed a new clustering ensemble framework based on weight. The author substantiate the framework on various datasets, whose results show its validity and excellent performance on all considered datasets.

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