Paper Info
Title
Automatic Clustering Based On Crow Search Algorithm-Kmeans (Csa-Kmeans) And Data Envelopment Analysis (Dea)
Abstract
Cluster Validity Indices (CVI) evaluate the efficiency of a clustering algorithm and Data Envelopment Analysis (DEA) evaluate the efficiency of Decision-Making Units (DMUs) using a number of inputs data and outputs data. Combination of the CVI and DEA inspired the development of a new automatic clustering algorithm called Automatic Clustering Based on Data Envelopment Analysis (ACDEA). ACDEA is able to determine the optimal number of clusters in four main steps. In the first step, a new clustering algorithm called CSA-Kmeans is introduced. In this algorithm, clustering is performed by the Crow Search Algorithm (CSA), in which the K-means algorithm generates the initial centers of the clusters. In the second step, the clustering of data is performed from k(min) cluster to k(max) cluster, using CSA-Kmeans. At each iteration of clustering, using correct data labels, Within-Group Scatter (WGS) index, Between-Group Scatter (BGS) index, Dunn Index (DI), the Calinski-Harabasz (CH) index, and the Silhouette index (SI) are extracted and stored, which ultimately these indices make a matrix that the columns of this matrix indicate the values of validity indices and the rows or DMUs represent the number of clustering times from k(min) cluster to k(max) cluster. In the third step, the efficiency of the DMUs is calculated using the DEA method based on the second stage matrix, and given that the DI, CH, and SI estimate the relationship within group scatter and between group scatter, WGS and BGS are used as input variables and the indices of DI, CH and SI are used as output variables to DEA. Finally, in step four, AP method is used to calculate the efficiency of DMUs, so that an efficiency value is obtained for each DMU that maximum efficiency represents the optimal number of clusters. In this study, three categories of data are used to measure the efficiency of the ACDEA algorithm. Also, the efficiency of ACDEA is compared with the DCPSO, GCUK and ACDE algorithms. According to the results, there is a positive significant relationship between input CVI and output CVI in data envelopment analysis, and the optimal number of clusters is achieved for many cases.
Year
DOI
Venue
2018
10.2991/ijcis.11.1.98
INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS
Keywords
Field
DocType
Automatic Clustering, K-Means, Crow Search Optimization Algorithm, Cluster Validity Indices, Data Envelopment Analysis
k-means clustering,Data envelopment analysis,Artificial intelligence,Crow search algorithm,Cluster analysis,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
11
1
1875-6891
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Alireza Balavand100.34
Ali Husseinzadeh Kashan2211.47
Abbas Saghaei3417.70