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A SURVEY ON PARTITION CLUSTERING ALGORITHMS
, 2011
"... Learning is the process of generating useful information from a huge volume of data. Learning can be classified as supervised learning and unsupervised learning. Clustering is a kind of unsupervised 1 International Journal of Enterprise Computing and Business Systems (Online) ..."
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Cited by 7 (0 self)
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Learning is the process of generating useful information from a huge volume of data. Learning can be classified as supervised learning and unsupervised learning. Clustering is a kind of unsupervised 1 International Journal of Enterprise Computing and Business Systems (Online)
Partitioning Clustering Algorithms for Data Stream Outlier Detection
"... ABSTRACT: Recently many researchers have focused on mining data streams and they proposed many techniques and algorithms for data streams. They are data stream classification, data stream clustering, and data stream frequent pattern items and so on. Data stream clustering techniques are highly help ..."
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ABSTRACT: Recently many researchers have focused on mining data streams and they proposed many techniques and algorithms for data streams. They are data stream classification, data stream clustering, and data stream frequent pattern items and so on. Data stream clustering techniques are highly
Measuring constraint-set utility for partitional clustering algorithms
- In: Proceedings of the Tenth European Conference on Principles and Practice of Knowledge Discovery in Databases
, 2006
"... Abstract. Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves the performance of a variety of algorithms. However, in most of these experiments, results are averaged ..."
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Cited by 49 (5 self)
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Abstract. Clustering with constraints is an active area of machine learning and data mining research. Previous empirical work has convincingly shown that adding constraints to clustering improves the performance of a variety of algorithms. However, in most of these experiments, results are averaged
Performance Issues on K-Mean Partitioning Clustering Algorithm
"... In data mining, cluster analysis is one of challenging field of research. Cluster analysis is called data segmentation. Clustering is process of grouping the data objects such that all objects in same group are similar and object of other group are dissimilar. In literature, many categories of clust ..."
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of cluster analysis algorithms present. Partitioning methods are one of efficient clustering methods, where data base is partition into groups in iterative relocation procedure. K-means is widely used partition method. In this paper, we presented the k-means algorithm and its mathematical calculations
• Some Partitioning Clustering Algorithms for Interval-Valued Data
"... • Adequacy criterion • Distance functions between vectors of intervals ..."
A PARTITIONAL CLUSTERING ALGORITHM FOR IMPROVING THE STRUCTURE OF OBJECT-ORIENTED SOFTWARE SYSTEMS
"... Abstract. In this paper we are focusing on the problem of program re-structuring, an important process in software evolution. We aim at intro-ducing a partitional clustering algorithm that can be used for improving software systems design. The proposed algorithm improve several clus-tering algorithm ..."
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Cited by 1 (0 self)
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Abstract. In this paper we are focusing on the problem of program re-structuring, an important process in software evolution. We aim at intro-ducing a partitional clustering algorithm that can be used for improving software systems design. The proposed algorithm improve several clus-tering
Effect of Distance measures on Partitional Clustering Algorithms using Transportation Data
"... Abstract — Similarity/dissimilarity measures in clustering algorithms play an important role in grouping data and finding out how well the data differ with each other. The importance of clustering algorithms in transportation data has been illustrated in previous research. This paper compares the ef ..."
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the effect of different distance/similarity measures on a partitional clustering algorithm kmedoid(PAM) using transportation dataset. A recently developed data mining open source software ELKI has been used and results illustrated.
Fuzzy Partition Clustering Algorithms Based on Alternative Mahalanobis Distances
- Proceedings of International conference on Machine Learning and Cybernetics 2007
, 2007
"... The well-known fuzzy partition clustering algorithms are mainly based on Euclidean distance measure for partitioning, which can only be used for the clusters in the data set with the same super-spherical shape distribution. Instead of using Euclid-ean distance measure, Gustafson & Kessel (1979) ..."
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Cited by 1 (0 self)
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The well-known fuzzy partition clustering algorithms are mainly based on Euclidean distance measure for partitioning, which can only be used for the clusters in the data set with the same super-spherical shape distribution. Instead of using Euclid-ean distance measure, Gustafson & Kessel (1979
Find-k: A New Algorithm for Finding the k in Partitioning Clustering Algorithms
"... Abstract:- Document clustering is an automatic grouping of text documents into clusters. These documents are clustered in such a way that documents within a cluster have high similarity in comparison to one another, and are dissimilar to documents in other clusters. Fast and high-quality document cl ..."
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clustering algorithms play an important role in providing intuitive navigation and browsing mechanisms by organizing large amounts of information into a small number of meaningful clusters. In partitioning based algorithms, such as k-means and k-medoids, it is necessary to fix the value of k prior
Efficient Routing using Partitive Clustering Algorithms in Ferry-based Delay Tolerant Networks
"... The Delay Tolerant Networks (DTNs) generally contain relatively sparse nodes that are frequently disconnected. Message Ferrying (MF) is a mobility-assisted approach which utilizes a set of mobile elements to provide communication service in ferry-based DTNs. In this paper, we propose a Density-Aware ..."
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Cited by 1 (0 self)
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-Aware Route Design (DARD) algorithm using partitive clustering algorithms along a validity index for identifying the suitable node clusters and assigning ferries to these clusters. In the proposed algorithm, unlike using multiple ferries in a single route (SIRA algorithm) or dividing the deployment area
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