Sawsan Al-Malak
Manar Hosny
ABSTRACT
Clustering is widely used in a variety of fields to find structures among data and extract useful knowledge. Recently, there has been an emergent need for robust and efficient techniques that can manage the exploding volume of data available in the World Wide Web or gathered from devices and sensors. However, clustering such data is challenging, due to the multimodal nature of this information. In this work, we introduce a novel Multimodal Adaptive Genetic Clustering (MAGC) algorithm that clusters information based on multiple features. Our approach adds feature weights as an extension to the chromosome, which represents a clustering solution, such that feature weights are also evolved and optimized alongside the original clustering solution. The number of clusters is also adaptive and is optimized during the search.
- Berkhin, P. A survey of clustering data mining techniques. In Grouping multidimensional data. Springer Berlin Heidelberg, 2006, 25--71.Google Scholar
- Bolettieri, P., Esuli, A., Falchi, F., et al. CoPhIR: a test collection for content-based image retrieval. abs/0905.4627, (2009).Google Scholar
- Davies, D.L. and Bouldin, D.W. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1, (1979), 224--227. Google ScholarDigital Library
- De Jong, K.A. Analysis of the behavior of a class of genetic adaptive systems. Doctoral dissertation, University of Michigan, Dissertation Abstracts International 36, 10 (1975). Google ScholarDigital Library
- Falkenauer, E. Genetic algorithms and grouping problems. John Wiley & Sons, Inc., New York, NY, USA, 1998. Google ScholarDigital Library
- Hruschka, E.R., Campello, R.J., Freitas, A.A., and De Carvalho, A.C. A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39, 2 (2009), 133--155. Google ScholarDigital Library
- Mardia, K.V., Kent, J.T., and Bibby, J.M. Multivariate analysis. Academic Press, (1979).Google Scholar
- Nikolopoulos, S., Giannakidou, E., Kompatsiaris, I., Patras, I., and Vakali, A. Combining Multi-modal Features for Social Media Analysis. In S.M. Modeling, S.C.H.H. Computing, J. Luo, et al., eds., Social Media Modeling and Computing. Springer London, 2011, 71--96.Google Scholar
- Petrovic, S. A comparison between the silhouette index and the davies-bouldin index in labelling ids clusters. Proceedings of the 11th Nordic Workshop of Secure IT Systems, (2006), 53--64.Google Scholar
- Piatrik, T. and Izquierdo, E. Subspace clustering of images using Ant colony Optimisation. 16th IEEE International Conference on Image Processing (ICIP), Cairo, (2009), 229--232. Google ScholarDigital Library
Index Terms
- A Multimodal Adaptive Genetic Clustering Algorithm
Recommendations
A genetic clustering algorithm using a message-based similarity measure
In this paper, a genetic clustering algorithm is described that uses a new similarity measure based message passing between data points and the candidate centers described by the chromosome. In the new algorithm, a variable-length real-value chromosome ...
Analysis and Comparison of Clustering Techniques for Chronic Kidney Disease With Genetic Algorithm
The purpose of this article is to weigh up the foremost imperative features of Chronic Kidney Disease CKD. This study is based mostly on three cluster techniques like; K means, Fuzzy c-means and hierarchical clustering. The authors used evolutionary ...
A Genetic Algorithm Using Hyper-Quadtrees for Low-Dimensional K-means Clustering
The k-means algorithm is widely used for clustering because of its computational efficiency. Given n points in d\hbox{-}{\rm{dimensional}} space and the number of desired clusters k, k-means seeks a set of k cluster centers so as to minimize the sum of ...
Comments