International Journal of Intelligent Information Technologies, Special
... a dataset. The problem is that none of them is satisfactory for all kinds of cluster analysis (Dudoit & Fridlyand, 2002; Stehl, 2002). One reason may be that people have different opinions about the granularity of clusters and there may be several right answers to k with respect to different desired ...
... a dataset. The problem is that none of them is satisfactory for all kinds of cluster analysis (Dudoit & Fridlyand, 2002; Stehl, 2002). One reason may be that people have different opinions about the granularity of clusters and there may be several right answers to k with respect to different desired ...
Ontology-based Distance Measure for Text Clustering
... greater than 11 . FW-KMeans [20] is a subspace clustering algorithm that identifies clusters from subspaces by automatically assigning large weights to the features that form the subspaces in which the clusters are formed. The new algorithm is based on the extensions to the standard kmeans algorithm ...
... greater than 11 . FW-KMeans [20] is a subspace clustering algorithm that identifies clusters from subspaces by automatically assigning large weights to the features that form the subspaces in which the clusters are formed. The new algorithm is based on the extensions to the standard kmeans algorithm ...
International Journal of Advance Research in Computer Science
... Clustering: Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Clustering can also be considered the most important unsupervised learning technique; so ...
... Clustering: Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. Clustering can also be considered the most important unsupervised learning technique; so ...
pattern discovery and document clustering using k-means
... Documents are generally in big volumes, high dimension and with complex semantics which are challenging problems of document clustering. Our motive in this present paper is to extract particular domain of work from a huge collection of documents using popular document clustering methods. Agglomerati ...
... Documents are generally in big volumes, high dimension and with complex semantics which are challenging problems of document clustering. Our motive in this present paper is to extract particular domain of work from a huge collection of documents using popular document clustering methods. Agglomerati ...
Cluster - KDD - Kansas State University
... objects) in a set of meaningful sub-classes, called clusters Helps users understand the natural grouping or structure in a data set ...
... objects) in a set of meaningful sub-classes, called clusters Helps users understand the natural grouping or structure in a data set ...
Scalable Clustering Algorithms with Balancing Constraints
... produced does not differ significantly from the one that would be obtained with full data. The computational complexity of several of these methods are linear per iteration in the number of data points N as well as the number of clusters k and hence scale very well. However their “sequential cluster ...
... produced does not differ significantly from the one that would be obtained with full data. The computational complexity of several of these methods are linear per iteration in the number of data points N as well as the number of clusters k and hence scale very well. However their “sequential cluster ...
Subspace Clustering using CLIQUE: An Exploratory Study
... “irrelevant dimensions”, “distance problem” etc. To cluster higher dimensional data, density and grid based, both traditional clustering algorithms combined and let to a step ahead to the traditional clustering i.e. called subspace clustering. This paper presents an important subspace clustering alg ...
... “irrelevant dimensions”, “distance problem” etc. To cluster higher dimensional data, density and grid based, both traditional clustering algorithms combined and let to a step ahead to the traditional clustering i.e. called subspace clustering. This paper presents an important subspace clustering alg ...
Scalable Clustering Methods for the Name Disambiguation Problem
... – We viewed a name disambiguation, which frequently occurs in digital libraries and on the web, as a hard clustering problem. To apply clustering algorithm, we categorized major clustering methods into two classes: hierarchical clustering methods and partitive clustering methods. Then, we further st ...
... – We viewed a name disambiguation, which frequently occurs in digital libraries and on the web, as a hard clustering problem. To apply clustering algorithm, we categorized major clustering methods into two classes: hierarchical clustering methods and partitive clustering methods. Then, we further st ...
Outlier Detection Using High Dimensional Dataset for
... attributes, known as spatial data. Some algorithms work with data indirectly by constructing summaries of data over the attribute space subsets. They perform space segmentation and then aggregate appropriate segments. Categorical data is intimately connected with transactional databases. The concept ...
... attributes, known as spatial data. Some algorithms work with data indirectly by constructing summaries of data over the attribute space subsets. They perform space segmentation and then aggregate appropriate segments. Categorical data is intimately connected with transactional databases. The concept ...
Software Quality Analysis with Clustering Method
... computed and the defect set with effort nearest to the average forms the first defect set in the MODERATE cluster. Now each cluster consists of one defect set. 5. Next each defect set is assigned to only one of the clusters. Each defect set is assigned to the nearest cluster by computing its distanc ...
... computed and the defect set with effort nearest to the average forms the first defect set in the MODERATE cluster. Now each cluster consists of one defect set. 5. Next each defect set is assigned to only one of the clusters. Each defect set is assigned to the nearest cluster by computing its distanc ...
A Comparative Study of Issues in Big Data Clustering Algorithm with
... classified into two main categories: hierarchical method and partitioning methods. Hierarchical methods are either agglomerative or divisive. Given n objects to be clustered, agglomerative methods begin with n clusters. In each step, two clusters are chosen and merged. This process continuous until ...
... classified into two main categories: hierarchical method and partitioning methods. Hierarchical methods are either agglomerative or divisive. Given n objects to be clustered, agglomerative methods begin with n clusters. In each step, two clusters are chosen and merged. This process continuous until ...
Nearest-neighbor chain algorithm
In the theory of cluster analysis, the nearest-neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be clustered and an amount of time linear in the number of distinct distances between pairs of points. The main idea of the algorithm is to find pairs of clusters to merge by following paths in the nearest neighbor graph of the clusters until the paths terminate in pairs of mutual nearest neighbors. The algorithm was developed and implemented in 1982 by J. P. Benzécri and J. Juan, based on earlier methods that constructed hierarchical clusterings using mutual nearest neighbor pairs without taking advantage of nearest neighbor chains.