Dynamic Dispatch Cluster Ensemble Approach for Mixed Attributes Dataset

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-48 Number-2
Year of Publication : 2017
Authors : Waale Angela Gboraloo, Chidiebere Ugwu
DOI :  10.14445/22312803/IJCTT-V48P120


Waale Angela Gboraloo, Chidiebere Ugwu "Dynamic Dispatch Cluster Ensemble Approach for Mixed Attributes Dataset". International Journal of Computer Trends and Technology (IJCTT) V48(2):96-102, June 2017. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.

Abstract -
In recent time, data is growing binomially in almost all organizations in the world such as schools, hospitals, banks, which are usually of mixed attribute data values with numerical or categorical attribute data type. Several clustering systems with various clustering algorithms has been proposed to discover useful patterns that exist in such datasets, all adopting the same approach of splitting the dataset into two fragmented files and storing them on the storage device before subjecting them to clustering algorithms. This approach slows down the clustering process when there is large dataset. This paper presents a new dynamic dispatch cluster ensemble approach to clustering mixed attribute dataset based on ensemble technique where the attribute data type is automatically detected at run-time in place of outright splitting of the dataset into two subsets before clustering. The system utilized k means and Squeezer algorithms for clustering the various datasets. Object oriented design and Java programming language were used in the system development and implementation. The system was experimented on real life dataset obtained from UCL machine learning repository and results obtained were significantly different when compared to existing clustering systems. The process time was faster than the old systems because of the implicit and not explicit approach adopted in the system designs.

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Mixed Attributes Dataset, Clustering, Data Mining, Dynamic Dispatch and Cluster Ensemble.