Paper Title : A LION OPTIMIZATION BASED K-PROTOTYPE CLUSTERING ALGORITHM FOR MIXED DATA

ISSN : 2394-2231
Year of Publication : 2022

10.5281/zenodo.6410014

Authors: Mr.C.Mani M.C.A.,M.Phil.,M.E., C. Mehala

         



MLA Style: A LION OPTIMIZATION BASED K-PROTOTYPE CLUSTERING ALGORITHM FOR MIXED DATA " Mr.C.Mani M.C.A.,M.Phil.,M.E., C. Mehala " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: A LION OPTIMIZATION BASED K-PROTOTYPE CLUSTERING ALGORITHM FOR MIXED DATA " Mr.C.Mani M.C.A.,M.Phil.,M.E., C. Mehala " Volume 9 - Issue 2 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
Data Mining is used to gather information from huge set of data. Clustering is a grouping task for a set of objects. Clustering algorithms are divided by several types including hierarchical clustering algorithms,partitioning clustering and density based. The partitioning clustering includes K-Means clustering, K-Modes Clustering and CLARA algorithm. The K-Means clustering is only used for numeric data which has original optima. The K-Modes extends to the K-Means when the sphere is categorical. One of the most important algorithms for clustering heterogeneous type of data is the K- Prototype algorithm. This algorithm is veritably salutary for clustering large data sets. One of the simple optimization methods is Lion Optimization, that could be applied effectively for enhancing clustering results. It's useful for handling mixed data set. This leads a good optimization to calculate the centroid with K- Prototype clustering method. To overcome the problem in this clustering, Lion optimization Algorithm can be used. The proposed algorithm is enforced on standard standard dataset taken from UCI Machine Learning Repository. The Lion Optimization grounded KPrototype clustering algorithm yields a better result when compared with the K- Prototype clustering.

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Keywords
— Kmeans Clustering, Lion Optimization, Data Mining, Machine Learning.