Add to ORKGClustering involves the partitioning of n objects into k clusters. Many clustering algorithms use hard-partitioning techniques where each object is assigned to one cluster. In this paper we propose an overlapping algorithm MCOKE which allows objects to belong to one or more clusters. The algorithm is different from fuzzy clustering techniques because objects that overlap are assigned a membership value of 1 (one) as opposed to a fuzzy membership degree. The algorithm is also different from other overlapping algorithms that require a similarity threshold be defined a priori which can be difficult to determine by novice users
This article deals with the development of an im-proved clustering technique that is based on the id...
When confronted to a clustering problem, one has to choose which algorithm to run. Building a system...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use ha...
Data Clustering or unsupervised classification is one of the main research area in Data Mining. Part...
Overlapping between clusters is a major issue in clustering. In this cluster configuration, an objec...
Most natural world data involves overlapping communities where an object may belong to one or more c...
Traditional clustering algorithms, such as k-means, output a clustering that is disjoint and exhaust...
Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) i...
Technically, the problem of overlap in a dataset is viewed as an uncertainty problem and is solved ...
Given a dataset, traditional clustering algorithms often only provide a single partitioning or a sin...
Determining the number of clusters is one of the most important topics in cluster analysis. The abil...
Clustering is one of the major and interesting tools for many data analysis in business, science, me...
Relative geometric arrangements of the sample points, with reference to the structure of the imbeddi...
MCOKE algorithm in identifying data objects to multi cluster is known for its simplicity and effecti...
This article deals with the development of an im-proved clustering technique that is based on the id...
When confronted to a clustering problem, one has to choose which algorithm to run. Building a system...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...
Clustering involves the partitioning of n objects into k clusters. Many clustering algorithms use ha...
Data Clustering or unsupervised classification is one of the main research area in Data Mining. Part...
Overlapping between clusters is a major issue in clustering. In this cluster configuration, an objec...
Most natural world data involves overlapping communities where an object may belong to one or more c...
Traditional clustering algorithms, such as k-means, output a clustering that is disjoint and exhaust...
Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) i...
Technically, the problem of overlap in a dataset is viewed as an uncertainty problem and is solved ...
Given a dataset, traditional clustering algorithms often only provide a single partitioning or a sin...
Determining the number of clusters is one of the most important topics in cluster analysis. The abil...
Clustering is one of the major and interesting tools for many data analysis in business, science, me...
Relative geometric arrangements of the sample points, with reference to the structure of the imbeddi...
MCOKE algorithm in identifying data objects to multi cluster is known for its simplicity and effecti...
This article deals with the development of an im-proved clustering technique that is based on the id...
When confronted to a clustering problem, one has to choose which algorithm to run. Building a system...
Advances in recent techniques for scientific data collection in the era of big data allow for the sy...