A Clustering Approach by SSPCO Optimization Algorithm Based on Chaotic Initial Population
سال انتشار: 1395
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 487
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شناسه ملی سند علمی:
JR_JECEI-4-1_005
تاریخ نمایه سازی: 23 دی 1396
چکیده مقاله:
Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. One of the things that smart algorithms are applied to solve is the problem of clustering. Clustering is employed as apowerful tool in many data mining applications, data analysis, and data compression in order to group data on the number of clusters (groups). In the present article, a chaotic SSPCO algorithm is utilized for clusteringdata on different benchmarks and datasets; moreover, clustering with artificial bee colony algorithm and particle mass 9 clustering technique is compared. Clustering tests have been done on 13 datasets from UCImachine learning repository. The results show that clustering SSPCO algorithm is a clustering technique which is very efficient in clusteringmultivariate data.
کلیدواژه ها:
نویسندگان
Rohollah Omidvar
Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran.
Hamid Parvin
Young Researchers and Elite Club, Nourabad Mamasani Branch, Islamic Azad University, Nourabad Mamasani,Iran.
Amin Eskandari
Sama Technical and Vocational Training College, Azad University of Shiraz, Shiraz, Iran.