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The GridOPTICS clustering algorithm [PDF]

Intelligent Data Analysis, 2016
The OPTICS algorithm is a hierarchical density-based clustering method. It creates reachability plots to identify all clusters in the point set. Nevertheless, it has limitation, namely it is very slow for large data sets. We introduce the GridOPTICS algorithm, which builds a grid structure to reduce the number of data points, then it applies the OPTICS
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A Practical Clustering Algorithm

2008
We present a novel clustering algorithm (SDSA algorithm) based on the concept of the short distance of the consecutive points and the small angle between the consecutive vectors formed by three adjacent points. Not only the proposed SDSA algorithm is suitable for almost all test data sets used by Chung and Liu for point symmetry-based K-means algorithm
Wei Li 0158, Haohao Li, Jianye Chen
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Adaptive Clustering Algorithms

2006
This paper proposes an adaptive clustering approach. We focus on re-clustering an object set, previously clustered, when the feature set characterizing the objects increases. We have developed adaptive extensions for two traditional clustering algorithms (k-means and Hierarchical Agglomerative Clustering).
Alina Campan, Gabriela Serban
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An Evolutionary Clustering Algorithm

2004
There are many heuristic algorithms for clustering, from which the most important are the hierarchical methods of agglomeration, especially the Ward’s method. Among the iterative methods the most universally used is the C–means method and it’s generalizations.
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Range clustering: An algorithm for empirical evaluation of classical clustering algorithms

2016 Ninth International Conference on Contemporary Computing (IC3), 2016
Cluster analysis is a principal method in analytics domain of data mining. The algorithm used for clustering directly influences the results obtained from applying the clustering algorithm (clusters). Data clustering is done in order to identify the patterns and trends not identifiable from just looking at the data. Clustering may be supervised (if the
Nishant Arora, Sandeep Jain, Santosh Kumar Verma   +2 more
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An automated clustering algorithm based on agglomerative clustering

2016 24th Signal Processing and Communication Application Conference (SIU), 2016
The most important one of main problems for K-based clustering algorithm is randomly selected k parameter when running the algorithm. In this study, an automated clustering algorithm based on agglomerative clustering and clusters without taking k parameter from user have been proposed.
Armagan Karabina, Erdal Kiliç
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How to “alternatize” a clustering algorithm

Data Mining and Knowledge Discovery, 2012
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mahmud Shahriar Hossain, Naren Ramakrishnan, Ian Davidson, Layne T. Watson   +3 more
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Genetic algorithms for clustering and fuzzy clustering

WIREs Data Mining and Knowledge Discovery, 2011
AbstractClustering has been an area of intensive research for several decades because of its multifaceted applications in innumerable domains. Clustering can be either Boolean, where a single data point belongs to exactly one cluster, or fuzzy, where a single data point can have nonzero belongingness to more than one cluster.
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A Cluster Algorithm Identifying the Clustering Structure

2008 International Conference on Computer Science and Software Engineering, 2008
Cluster analysis is a primary method for database mining. Most of clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-datasets there does not exist a global parameter setting for which the result of the clustering algorithm describes the ...
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A Robust Information Clustering Algorithm

Neural Computation, 2005
We focus on the scenario of robust information clustering (RIC) based on the minimax optimization of mutual information (MI). The minimization of MI leads to the standard mass-constrained deterministic annealing clustering, which is an empirical risk-minimization algorithm.
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