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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Zhu, Erzhou Wen, Peng Zhu, Binbin Liu, Feng Wang, Futian Li, Xuejun Anhui Univ Sch Comp Sci & Technol Hefei Anhui Peoples R China

ISBN: (纸本)9781728111414

clustering tries to find the natural structure of input datasets and partitions them into groups or clusters. As an unsupervised pattern classification method, it has been widely used in data mining, pattern recognition, image processing and so on. However, many of the existing clustering algorithms are suffering from many obstacles, such as low efficiency, poor clustering accuracy, more sensitive to noise points and cannot deal with complex big data properly. Aiming at these problems, an improved K-means algorithm (Grid-K-means) is firstly proposed. In the algorithm, dynamically changing grids operations are used to substitute data point operations to improve the clustering efficiency and reduce the number of manually setting initial parameters. Meanwhile, by utilizing grids with the highest density to determine the initial clustering centers, more accurate and stable clustering results are acquired. Then, based on the idea of utilizing grid as the weighted representative point to process the dataset, a new clustering validity index (BCVI) is introduced to better evaluate the quality of clustering results. BCVI can quickly determine the optimal clustering number especially for large-scale datasets. Experimental results on testing 5 simulated datasets (including two large sample data sets) have demonstrated that the Grid-K-means algorithm is faster and more accurate than the traditional ones. Meanwhile, the clustering results are evaluated by our BCVI and 6 other existing clustering validity indexes. The experimental results have also shown that the new BCVI is superior to traditional indexes in data processing speed and stability.

关键词： partitioning clustering algorithm Cluster Validity Index Optimal clustering Number Big Data

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clustering for High Dimensional Data 1

Clustering for High Dimensional Data

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1st International Conference on Networks & Soft Computing (ICNSC)

作者： Sharma, Varun Kumar Bala, Anju Thapar Univ Dept Comp Sci & Engn Patiala Punjab India

ISBN: (纸本)9781479934867

clustering is an exploratory data analysis technique, which categorizes the dataset into sonic groups. These groups are formed in a way so that items which have similar features live in same group and those have dissimilar features remain in other. There are many clustering algorithm available. Different kinds of algorithms are best used for different kinds of data. K-means is most used clustering analysis algorithm. It is an iterative approach of point assignment into k clusters. It gives best result and is easily implementable. The k-means algorithm has many issues with it. The main issue is its high time complexity. Several improvements have been suggested by research community. But when it is applied on high dimensional data, the complexity becomes infeasible. In this paper, an approach to reduce the computation of distance function has been proposed. It aims to define a cluster membership set for every cluster. The distance function is calculated only for the clusters which are contained in this set. With this membership set of cluster, the complexity of overall algorithm is reduced.

关键词： Data Mining clustering partitioning clustering algorithm k-means algorithm Initial centroid High Dimensional Data

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An Improved Parameter less Data clustering Technique based on Maximum Distance of Data and Lioyd k-means algorithm

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Procedia Technology 2012年 1卷 367-371页

作者： Wan Maseri Binti Wan Mohd A.H. Beg Tutut Herawan K.F. Rabbi Faculty of Computer Systems and Software Engineering University Malaysia Pahang Gabnang-26300 Pahang Malaysia

K-means algorithm is very well-known in large data sets of clustering. This algorithm is popular and more widely used for its easy implementation and fast working. However, it is well known that in the k-means algorithm, the user should specify the number of clusters in advance. In order to improve the performance of the K-means algorithm, various methods have been proposed. In this paper, has been presented an improved parameter less data clustering technique based on maximum distance of data and Lioyd k-means algorithm. The experimental results show that the use of new approach to defining the centroids, the number of iterations has been reduced where the improvement was 60%.

关键词： K-Means algorithm clustering partitioning clustering algorithm Data Mining

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SDCC: A New Stable Double-Centroid clustering Technique Based on K-Means for Non-spherical Patterns

SDCC: A New Stable Double-Centroid Clustering Technique Base...

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6th International Symposium on Neural Networks

作者： Chang, Juifang Natl Kaohsiung Univ Appl Sci Dept Int Business Kaohsiung 80778 Taiwan

ISBN: (纸本)9783642015090

Numerous existing partitioning clustering algorithms, such as K-means. are developed to discover clusters that fit some of the static models. These algorithms may fail if it chooses a set of incorrect parameters in the static model with respect to the objects being clustered, or when the objects consist of patterns that are of non-spherical or not the same size. Furthermore, they could produce an instable result. This investigation presents a new partition clustering algorithm named SDCC. which can improve the problem of instable results in partitioning-based clustering, Such as K-means. As a hybrid approach that utilizes double-centroid concept, the proposed algorithm can eliminate the above-mentioned drawbacks to produce stable results while recognizing the non-spherical patterns and clusters that are not the same size. Experimental results illustrate that the new algorithm can identify non-spherical pattern correctly, and efficiently reduces the problem of long Computational time when applying KGA and GKA. It also indicates thin the proposed approach produces much smaller errors than K-means, KGA and GKA approaches in most cases examined herein.

关键词： Data clustering Data mining partitioning clustering algorithm

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