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169
Visually Interacting with k-medoid-based Algorithms ⋆
"... Abstract. Although database management systems supply basic tools to data recovery and analysis through standard transactions in large amounts of data, the analysis of these data in high dimensional spaces is an overwhelming job. On the other hand, human beings have a great capacity to quickly absor ..."
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absorb and understand information presented in graphical form. As data clustering is one of the key techniques in the KDD process, this work aims to use the human visual perception to help in the iterative clustering analysis done by the k-medoid-based algorithms. Our technique takes advantages
Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids
, 2006
"... Spatial clustering has been an active research area in Spatial Data Mining (SDM). Many methods on spatial clustering have been proposed in the literature, but few of them have taken into account constraints that may be present in the data or constraints on the clustering. In this paper, we discuss t ..."
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Cited by 5 (0 self)
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the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on Genetic Algorithms (GAs) and K-Medoids, called GKSCOC, which aims to cluster spatial data with obstacles constraints. The GKSCOC algorithm can not only give attention to higher local
An Efficient Density based Improved K- Medoids Clustering algorithm
"... Abstract — Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many ..."
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Abstract — Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many
K-Medoids based Clustering of PlanetLab’s Slice-Centric Data
, 2013
"... Abstract: This paper investigates the application of widely used K-Medoids based clustering algorithm on data collected through CoMon facility for the PlanetLab testbed. The averaged values of various metrics in passively collected slice-centric data has been considered for clustering purposes. Vari ..."
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Abstract: This paper investigates the application of widely used K-Medoids based clustering algorithm on data collected through CoMon facility for the PlanetLab testbed. The averaged values of various metrics in passively collected slice-centric data has been considered for clustering purposes
Cluster Integrated Updation Strategies for ACO Algorithms
"... Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make probabilist ..."
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Ant Colony Optimization (ACO) algorithm has evolved as the most popular way to attack the combinatorial problems. The ACO algorithm employs multi agents called ants that are capable of finding optimal solution for a given problem instances. These ants at each step of the computation make
Supervised Clustering – Algorithms and Benefits
- In proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI04) , Boca
, 2004
"... This paper centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified and has the goal of identifying class-uniform clusters that have high probability densities. Four representative–based algo ..."
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Cited by 26 (14 self)
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fast medoid-based top-down splitting algorithm, named TDS. The four algorithms were evaluated using a benchmark consisting of four UCI machine learning data sets. In general, it seems that “greedy ” algorithms, such as SPAM, SRIDHCR, and TDS, do not perform particularly well for supervised clustering
Unsupervised Updation Strategies for ACO Algorithms
"... Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges to the o ..."
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Ant Colony Optimization (ACO) algorithms belong to class of metaheuristic algorithms, where a search is made for optimized solution rather than exact solution, based on the knowledge of the problem domain. ACO algorithms are iterative in nature. As the iteration proceeds, solution converges
A Fingertip and Webbed Point Detection Method Based on K-vector and K-medoids Clustering Algorithm
"... Abstract—The paper presents a calculation method to get the probable fingertips and webbed points in outline. First, use K-vectoe to calculate the K-slope of all points in outline and get a series of peak point sets. Then, cluster the peak point sets which have been obstained by K-medoids algorithm ..."
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Abstract—The paper presents a calculation method to get the probable fingertips and webbed points in outline. First, use K-vectoe to calculate the K-slope of all points in outline and get a series of peak point sets. Then, cluster the peak point sets which have been obstained by K-medoids algorithm
Parallel multi-swarm PSO based on K-medoids and uniform design
- Research Journal of Applied Sciences, Engineering and Technology
, 2013
"... Abstract: PAM (Partitioning around Medoid) is introduced to divide the swarm into several different subpopulations. PAM is one of k-medoids clustering algorithms based on partitioning methods. It attempts to divide n objects into k partitions. This algorithm overcomes the drawbacks of being sensiti ..."
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Cited by 2 (2 self)
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Abstract: PAM (Partitioning around Medoid) is introduced to divide the swarm into several different subpopulations. PAM is one of k-medoids clustering algorithms based on partitioning methods. It attempts to divide n objects into k partitions. This algorithm overcomes the drawbacks of being
A Incremental Multi-Centroid, Multi-Run Sampling Scheme for k-medoids-based Algorithms
- Proceedings of the Third International Conference on Data Mining Methods and Databases, Data Mining III
, 2002
"... Data clustering has become an important task for discovering significant patterns and characteristics in large spatial databases. The Multi-Centroid, Multi-Run Sampling Scheme (MCMRS) has been shown to be effective in improving the k-medoids-based clustering algorithms in our previous work. In this ..."
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Cited by 3 (0 self)
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. In this paper, a more advanced sampling scheme termed the Incremental (IMCMRS) is proposed for k-medoids-based clustering algorithms. Experimental results demonstrate the proposed scheme can not only reduce by more than 80% computation time but also reduce the average distance per object compared with CLARA
Results 1 - 10
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169
