Add to ORKGThis paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Merging for Optimal Clusters (AMOC) which aims to generate nearly optimal clusters for the given datasets automatically. The AMOC is an extension to standard k-means with a two phase iterative procedure combining certain validation techniques in order to find optimal clusters with automation of merging of clusters. Experiments on both synthetic and real data have proved that the proposed algorithm finds nearly optimal clustering structures in terms of number of clusters, compactness and separation
This paper introduces k\u27-means algorithm that performs correct clustering without pre-assigning t...
215 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.The study of the properties o...
In this paper, we introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Obje...
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Mergin...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Abstract: K-Means is the most popular clustering algorithm with the convergence to one of numerous ...
We propose a clustering method which produces valid results while automatically determining an optim...
The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity....
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
Cluster analysis method is one of the most analytical methods of data mining. The method will direct...
In solving the clustering problem, traditional methods, for example, the K-means algorithm and its v...
Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years d...
This paper presents a comprehensive review of existing techniques of k-means clustering algorithms m...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
Abstract—This paper introduces an optimized version of the standard K-Means algorithm. The optimizat...
This paper introduces k\u27-means algorithm that performs correct clustering without pre-assigning t...
215 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.The study of the properties o...
In this paper, we introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Obje...
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Mergin...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Abstract: K-Means is the most popular clustering algorithm with the convergence to one of numerous ...
We propose a clustering method which produces valid results while automatically determining an optim...
The traditional clustering algorithm, K-means, is famous for its simplicity and low time complexity....
Partitioning data into a finite number of k homogenous and separate clusters (groups) without use of...
Cluster analysis method is one of the most analytical methods of data mining. The method will direct...
In solving the clustering problem, traditional methods, for example, the K-means algorithm and its v...
Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years d...
This paper presents a comprehensive review of existing techniques of k-means clustering algorithms m...
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract us...
Abstract—This paper introduces an optimized version of the standard K-Means algorithm. The optimizat...
This paper introduces k\u27-means algorithm that performs correct clustering without pre-assigning t...
215 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2004.The study of the properties o...
In this paper, we introduce a new Multi-Objective Clustering algorithm (MOCA). The use of Multi-Obje...