We propose a clustering method which produces valid results while automatically determining an optimal number of clusters. The proposed method achieves these results with minimal user input, of which none pertains to a number of clusters. Our method\u2019s effectiveness in clustering, including its ability to produce valid results on data sets presenting nested or interlocking shapes, is demonstrated and compared with cluster validity analysis to other methods to which a known optimal number of clusters is provided, and to other automatic clustering methods. Depending on the particularities of the data set used, our method has produced results which are roughly equivalent or better than those of the compared methods.Peer reviewed: YesNRC pu...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering is one of the most popular artificial intelligence techniques which aims at identifying g...
Data Clustering is defined as grouping together objects which share similar properties. These proper...
The objective of data mining is to take out information from large amounts of data and convert it in...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to i...
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Mergin...
In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierar...
Hierarchical clustering constructs a hierarchy of clusters by either repeatedly merging two smaller ...
Clustering is a central topic in unsupervised learning and has a wide variety of applications. Howev...
. Clustering is an important data mining task which helps in finding useful patterns to summarize th...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
Determining an optimal number of clusters and producing reliable results are two challenging and cri...
Abstract: A fundamental and difficult problem in cluster analysis is the determination of the “true...
Clustering is one of the most popular artificial intelligence techniques which aims at identifying g...
Data Clustering is defined as grouping together objects which share similar properties. These proper...
The objective of data mining is to take out information from large amounts of data and convert it in...
Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mini...
Abstract — Clustering techniques have a wide use and importance nowadays. This importance tends to i...
This paper proposes a simple, automatic and efficient clustering algorithm, namely, Automatic Mergin...
In this paper, we propose a parameter-insensitive data partitioning approach for Chameleon, a hierar...
Hierarchical clustering constructs a hierarchy of clusters by either repeatedly merging two smaller ...
Clustering is a central topic in unsupervised learning and has a wide variety of applications. Howev...
. Clustering is an important data mining task which helps in finding useful patterns to summarize th...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
ia that provide significant distinctions between clustering methods and can help selecting appropria...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...