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A Survey of Partition based Clustering Algorithms in Data Mining: An Experimental Approach  [PDF]
T. Velmurugan,T. Santhanam
Information Technology Journal , 2011,
Abstract: Clustering is one of the most important research areas in the field of data mining. Clustering means creating groups of objects based on their features in such a way that the objects belonging to the same groups are similar and those belonging in different groups are dissimilar. Clustering is an unsupervised learning technique. Data clustering is the subject of active research in several fields such as statistics, pattern recognition and machine learning. From a practical perspective clustering plays an outstanding role in data mining applications in many domains. The main advantage of clustering is that interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. Clustering algorithms can be applied in many areas, for instance marketing, biology, libraries, insurance, city-planning, earthquake studies and www document classification. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique computational requirements on relevant clustering algorithms. A variety of algorithms have recently emerged that meet these requirements and were successfully applied to real-life data mining problems. They are subject of this survey. Also, this survey explores the behavior of some of the partition based clustering algorithms and their basic approaches with experimental results.
A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms
D. Thilagavathi ,Dr. Antony Selvadoss Thanamani
International Journal of Computer Trends and Technology , 2012,
Abstract: Various sciences can benefit from the use of grids to solve CPU-intensive problems, creating potential benefits to the entire society. Job scheduling is an integrated part of parallel and distributed computing. It allows selecting correct match of resource for a particular job and thus increases the job throughput and utilization of resources. Job should be scheduled in an automatic way to make the system more reliable, accessible and less sensitive to subsystem failures. This paper provides a survey on various heuristic algorithms, used for scheduling in grid.
A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms  [PDF]
D. Thilagavathi,Antony Selvadoss Thanamani
Computer Science , 2014,
Abstract: Computational Grids are a new trend in distributed computing systems. They allow the sharing of geographically distributed resources in an efficient way, extending the boundaries of what we perceive as distributed computing. Various sciences can benefit from the use of grids to solve CPU-intensive problems, creating potential benefits to the entire society. Job scheduling is an integrated part of parallel and distributed computing. It allows selecting correct match of resource for a particular job and thus increases the job throughput and utilization of resources. Job should be scheduled in an automatic way to make the system more reliable, accessible and less sensitive to subsystem failures. This paper provides a survey on various heuristic algorithms, used for scheduling in grid.
Methods of Hierarchical Clustering  [PDF]
Fionn Murtagh,Pedro Contreras
Computer Science , 2011,
Abstract: We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.
Selection of Clustering Algorithms Based on Grid-Connected Graph
挑选聚类算法的网格连通图方法

LI Xiang-Yu,WANG Kai-Jun,GUO Gong-De,
李翔宇
,王开军,郭躬德

计算机系统应用 , 2012,
Abstract: Each clustering algorithm has its suitable treatment of specific distribution data set. The SCGG method based on the Grid-Connected Graph is proposed to select suitable clustering algorithm for unknown distribution data set. The SCGG method analyses the potential structure of the data set. If the data set has ring clustering structure, the method select a single hierarchiral clustering algorithm, otherwise it selects the k-means. Experiment results show that SCGG is very efficient and successful.
Incremental clustering algorithm based on grid
一种基于网格的增量聚类算法

YIN Gui-sheng,YU Xiang,NING Hui,
印桂生
,于翔,宁慧

计算机应用研究 , 2009,
Abstract: This paper analyzed the existing clustering algorithms based on grid,and the clustering algorithms based on grid had the advantages of dealing with high dimensional data and high efficiency. However, traditional algorithms based on grid were influenced greatly by the granularity of grid partition.It proposed an incremental clustering algorithm based on grid, which was called IGrid.IGrid had the advantage of high efficiency of traditional clustering algorithms based on grid, and it also partitioned the grid space by dimensional radius in a dynamic and incremental manner to improve the quality of clustering.The experiments on real datasets and synthetic datasets show that IGrid has better performance than traditional clustering algorithms based on grid in both speed and accuracy.
Novel clustering algorithm based on grid and density
一种新型的基于密度和栅格的聚类算法*

XIONG Shi-yong,
熊仕勇

计算机应用研究 , 2011,
Abstract: In view of the efficiency and quality issues existed in both the grid and density clustering algorithms, this paper proposed the combination of density and grid clustering algorithm, that is DGCA (density and grid based clustering algorithm) which based on density and grid. The given algorithm firstly divides data space into grids; followed by storing data into the grid cell, and uses DBSCAN to conduct clustering mining; finally, it carries on clustering merging and elimination of noise points, and maps the local clustering results to the global clustering results. The experiment was theoretically varified with artificial data set on this clustering algorithm, and showed that the algorithm gained enhance on both time efficiency and clustering quality.
A Mean Approximation Approach to a Class of Grid-Based Clustering Algorithms
一类数据空间网格化聚类算法的均值近似方法

LI Cun-Hua,SUN Zhi-Hui,
李存华
,孙志挥

软件学报 , 2003,
Abstract: In recent years, the explosively growing amount of data in numerous clustering tasks has attracted considerable interest in boosting the existing clustering algorithms to large datasets. In this paper, the mean approximation approach is discussed to improve a spectrum of partition-oriented density-based algorithms. This approach filters out the data objects in the crowded grids and approximates their influence to the rest by their gravity centers. Strategies on implementation issues as well as the error bound of the mean approximation are presented. Mean approximation leads to less memory usage and simplifies computational complexity with minor lose of the clustering accuracy. Results of exhaustive experiments reveal the promising performance of this approach.
DATA CLUSTERING ALGORITHMS – A SURVEY
P.PRABHU,N.ANBAZHAGAN
Golden Research Thoughts , 2013, DOI: 10.9780/22315063
Abstract: Data clustering is the process of dividing data elements into classes or clusters so that items in the same class are as similar as possible, and items in different classes are as dissimilar as possible. Clustering is used in many areas, including artificial intelligence, biology, customer relationship management, data compression, data mining information retrieval, image processing, machine learning marketing, medicine, pattern recognition, psychology and statistics. This paper gives survey of various types of clustering algorithms. It describes its functionality, parameters needed and the time and space complexity required for clustering
Survey of clustering algorithms for MANET
Ratish Agarwal,Dr. Mahesh Motwani
International Journal on Computer Science and Engineering , 2009,
Abstract: Many clustering schemes have been proposed for ad hoc networks. A systematic classification of these clustering schemes enables one to better understand and make improvements. In mobile ad hoc networks, the movement of the network nodes may quickly change the topology resulting in the increase of the overhead message in topology maintenance. Protocols try to keep the number of nodes in a cluster around a pre-defined threshold to facilitate the optimal operation of the medium access control protocol. The clusterhead election is invoked on-demand, and is aimed to reduce the computation and communication costs. A large variety of approaches for ad hoc clustering have been developed by researchers which focus on different performance metrics. This paper presents a survey of different clustering schemes.
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