Book ChapterDOI
An Efficient K -Medoids-Based Algorithm Using Previous Medoid Index, Triangular Inequality Elimination Criteria, and Partial Distance Search
Shu-Chuan Chu,John F. Roddick,Jeng-Shyang Pan +2 more
- pp 63-72
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TLDR
A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search.Abstract:
Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.read more
Citations
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Journal ArticleDOI
Survey: Multi-criterion Pareto based particle swarm optimized polynomial neural network for classification: A review and state-of-the-art
S. Dehuri,S. B. Cho +1 more
TL;DR: An extensive experimental study has been carried out to compare the importance and effectiveness of the proposed MOPPSO method with the chosen state-of-the-art multi-objective particle swarm optimization (MOPSO) algorithm using several benchmark datasets.
Journal ArticleDOI
Accelerating k-medoid-based algorithms through metric access methods
TL;DR: This paper addresses the task of scaling up k-medoid-based algorithms through the utilization of metric access methods, allowing clustering algorithms to be executed by database management systems in a fraction of the time usually required by the traditional approaches.
Journal ArticleDOI
An Efficient Density based Improved K- Medoids Clustering algorithm
TL;DR: This paper proposes an efficient density based k-medoids clustering algorithm that will perform better than DBSCAN while handling clusters of circularly distributed data points and slightly overlapped clusters.
Journal ArticleDOI
A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining
TL;DR: A new data model for recording all the information of customer management is given, which transforms the traditional model-driven strategy to data-oriented method and a hybrid spatial clustering method named OETTC-MEANS-CLASA algorithm is proposed to solve site selection of the service center.
Proceedings ArticleDOI
Placement in Clouds for Application-Level Latency Requirements
TL;DR: This paper presents placement algorithms that exploit the Euclidean triangular inequality property of network topologies, and shows that Message Sequence Charts (MSCs), a widely-used mechanism for describing the execution of application procedures, can be naturally translated into the formalism of collective latency expressions.
References
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Some methods for classification and analysis of multivariate observations
TL;DR: The k-means algorithm as mentioned in this paper partitions an N-dimensional population into k sets on the basis of a sample, which is a generalization of the ordinary sample mean, and it is shown to give partitions which are reasonably efficient in the sense of within-class variance.
Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.
Proceedings Article
A density-based algorithm for discovering clusters in large spatial Databases with Noise
TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.
Book
Finding Groups in Data: An Introduction to Cluster Analysis
TL;DR: An electrical signal transmission system, applicable to the transmission of signals from trackside hot box detector equipment for railroad locomotives and rolling stock, wherein a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count.
BookDOI
Finding Groups in Data
TL;DR: In this article, an electrical signal transmission system for railway locomotives and rolling stock is proposed, where a basic pulse train is transmitted whereof the pulses are of a selected first amplitude and represent a train axle count, and a spike pulse of greater selected amplitude is transmitted, occurring immediately after the axle count pulse to which it relates, whenever an overheated axle box is detected.