A PSO Optimized Layered Approach for Parametric Clustering on Weather Dataset

Clustering is the process to present the data in an effective and organized way. There are number of existing clustering approaches but most of them suffer with problem of data distribution. If the distribution is non linear it gives impurities in clustering process. The propose work is about to improve the accuracy of such clustering algorithm. Here we are presenting the work on time series oriented database to present the work. Here we are presenting the three layer architecture, where first layer perform the partitioning based on time series and second layer will perform the basic clustering. The PSO is finally implemented to remove the impurities from the dataset.

[1]  Rehab F. Abdel-Kader.  Genetically Improved PSO Algorithm for Efficient Data Clustering , 2010, 2010 Second International Conference on Machine Learning and Computing.

[2]  Jinxin Dong,et al.  A New Clustering Algorithm Based on PSO with the Jumping Mechanism of SA , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[3]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[4]  Ming Chen,et al.  Notice of RetractionHybrid adaptive niche to improve particle swarm optimization clustering algorithm , 2011, 2011 Seventh International Conference on Natural Computation.

[5]  Brendan J. Frey,et al.  Non-metric affinity propagation for unsupervised image categorization , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[6]  Werasak Kurutach,et al.  Combination Artificial Ant Clustering and K-PSO Clustering Approach to Network Security Model , 2006, 2006 International Conference on Hybrid Information Technology.

[7]  H. Modares,et al.  Combining PSO and k-means to enhance data clustering , 2008, 2008 International Symposium on Telecommunications.

[8]  Gillian Dobbie,et al.  Particle Swarm Optimization Based Hierarchical Agglomerative Clustering , 2010, 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology.

[9]  Dongyun Wang,et al.  Clustering research of fabric deformation comfort using bi-swarm PSO algorithm , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[10]  Huiying Zhang,et al.  Research on Application of Clustering Algorithm Based on PSO for the Web Usage Pattern , 2007, 2007 International Conference on Wireless Communications, Networking and Mobile Computing.

[11]  Ying Zhang,et al.  X-SPA: Spatial Characteristic PSO Clustering Algorithm with Efficient Estimation of the Number of Cluster , 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.