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Search Results: 1 - 10 of 3473 matches for " clustering "
All listed articles are free for downloading (OA Articles)
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Unification of Subspace Clustering and Outliers Detection On High Dimensional Data
Sarala.R,Prakruthi.V,Prathibha Annapurna.P,Saranya.S
International Journal of Computer Technology and Applications , 2011,
Abstract: With the unanticipated requisites springing up in the data mining sector, it has become essential to group and classify patterns optimally in different objects based on their attributes, and detect the abnormalities in the object dataset. The grouping of similar objects can be best done with clustering based on the different dimensional attributes. When clustering high dimensional objects, the accuracy and efficiency of traditional clustering algorithms have been very poor, because objects may belong to different clusters in different subspaces comprised of different combinations of dimensions. By utilizing the subspace clustering as a method to initialize the centroids, and combine with fuzzy logic, this paper offers a fuzzy subtractive subspace clustering algorithm for automatically determining the optimal number of clusters. By our new Fuzzy Outlier detection and ranking approach, we detect and rank the outliers in heterogeneous high dimensional data. The experiment results show that the proposed clustering algorithm can give better cluster validation performance than the existing techniques.
Automatic Clustering Using Teaching Learning Based Optimization  [PDF]
M. Ramakrishna Murty, Anima Naik, J. V. R. Murthy, P. V. G. D. Prasad Reddy, Suresh C. Satapathy, K. Parvathi
Applied Mathematics (AM) , 2014, DOI: 10.4236/am.2014.58111
Abstract:

Finding the optimal number of clusters has remained to be a challenging problem in data mining research community. Several approaches have been suggested which include evolutionary computation techniques like genetic algorithm, particle swarm optimization, differential evolution etc. for addressing this issue. Many variants of the hybridization of these approaches also have been tried by researchers. However, the number of optimal clusters and the computational efficiency has still remained open for further research. In this paper, a new optimization technique known as “Teaching-Learning-Based Optimization” (TLBO) is implemented for automatic clustering of large unlabeled data sets. In contrast to most of the existing clustering techniques, the proposed algorithm requires no prior knowledge of the data to be classified rather it determines the optimal number of partitions of the data “on the run”. The new AUTO-TLBO algorithms are evaluated on benchmark datasets (collected from UCI machine repository) and performance comparisons are made with some well-known clustering algorithms. Results show that AUTO-TLBO clustering techniques have much potential in terms of comparative results and time of computations.

A Distributed Weighted Cluster Based Routing Protocol for MANETs  [PDF]
Naveen Chauhan, Lalit Kumar Awasthi, Narottam Chand, Vivek Katiyar, Ankit Chugh
Wireless Sensor Network (WSN) , 2011, DOI: 10.4236/wsn.2011.32006
Abstract: Mobile ad-hoc networks (MANETs) are a form of wireless networks which do not require a base station for providing network connectivity. Many MANETs’ characteristics that distinguish MANETs from other wireless networks also make routing a challenging task. Cluster based routing is a MANET routing schemes in which various clusters of mobile nodes are formed with each cluster having its own clusterhead which is responsible for routing among clusters. In this paper we propose and implement a distributed weighted clustering algorithm for MANETs. This approach is based on combined weight metric that takes into account several system parameters like the node degree, transmission range, energy and mobility of the nodes. We have evaluated the performance of the proposed scheme through simulation in various network situations. Simulation results show that improved distributed weighted clustering algorithm (DWCAIMP) outperforms the original distributed weighted clustering algorithm (DWCA).
A Projection Clustering Technique Based on Projection  [PDF]
Xiyu LIU, Xinjiang XIE, Wenping WANG
Journal of Service Science and Management (JSSM) , 2009, DOI: 10.4236/jssm.2009.24043
Abstract: Projection clustering is an important cluster problem. Although there are extensive studies with proposed algorithms and applications, one of the basic computing architectures is that they are all at the level of data objects. The purpose of this paper is to propose a new clustering technique based on grid architecture. Our new technique integrates minimum spanning tree and grid clustering together. By this integration of projection clustering with grid technique, the complexity of computing is lowered to O(NLogN).
VATdt: Visual Assessment of Cluster Tendency Using Diagonal Tracing  [PDF]
Yingkang Hu
American Journal of Computational Mathematics (AJCM) , 2012, DOI: 10.4236/ajcm.2012.21004
Abstract: The visual assessment of tendency (VAT) technique, for visually finding the number of meaningful clusters in data, developed by J. C. Bezdek, R. J. Hathaway and J. M. Huband, is very useful, but there is room for improvements. Instead of displaying the ordered dissimilarity matrix (ODM) as a 2D gray-level image for human interpretation as is done by VAT, we trace the changes in dissimilarities along the diagonal of the ODM. This changes the 2D data structure (matrices) into 1D arrays, displayed as what we call the tendency curves, which enables one to concentrate only on one variable, namely the height. One of these curves, called the d-curve, clearly shows the existence of cluster structure as patterns in peaks and valleys, which can be caught not only by human eyes but also by the computer. Our numerical experiments showed that the computer can catch cluster structures from the d-curve even in some cases where the human eyes see no structure from the visual outputs of VAT. And success on all numerical experiments was obtained us- ing the same (fixed) set of program parameter values.
Detection of the Process about Extreme Weather Events  [PDF]
Zhonghua Qian, Zengping Zhang, Guolin Feng
Journal of Applied Mathematics and Physics (JAMP) , 2013, DOI: 10.4236/jamp.2013.16002
Abstract:

In view of extreme values events mathematically rather than the process about extreme weather events, phase synchronization clustering method is introduced and the applicability of the method is discussed from the aspects of noise intensity and sequence length. At last the observed data is applied. The results show that clustering measure difference can detect the temporal process objectively to a certain degree and it has certain application to detect the temporal proc- ess of extreme weather events.

A Computational Bible Study of What to Love and What to Hate  [PDF]
Wei Hu
Advances in Literary Study (ALS) , 2014, DOI: 10.4236/als.2014.24019
Abstract: The Bible comprises the Old Testament of 39 books and the New Testament of 27 books. It can be viewed as the book of love, in which God revealed, out of His unconditional and unchanging love, His plan for the redemption of man in the Old Testament and fulfilled His promise made in the Old Testament by offering the one and only way of salvation through His son Jesus in the New Testament. In this study, we selected the Bible verses that contain the word love or its variation, which were then employed to cluster the books of the Bible with a computational approach. Of the 28 books containing the word love in the Old Testament, seven groups were identified: Genesis, Deuteronomy, Proverbs, Psalms, Song of Songs, First Samuel, and the rest of the other books. From the 26 books containing the word love in the New Testament, five groups were recognized: First Corinthians, John, First John, Luke and Mark and Matthew, and the rest of the other books. Furthermore, the major theme of love in each cluster was also elucidated. The opposite of love is hate. To gain the whole picture of love, we also selected Bible verses that contain the word hate or its variations. From clustering the books containing hate, different contexts of the word hate were recognized, teaching us to hate those that are contrary to love. Taken together, this computational study of the Bible demonstrated that God’s law is designed to love and to love fulfills the law completely and perfectly. Our findings provided a complete catalog of different contexts and themes in which the word love is being presented in the Bible, thereby enabling better understanding of the Bible in this regard.
A Combination Approach to Community Detection in Social Networks by Utilizing Structural and Attribute Data  [PDF]
Nasif Muslim
Social Networking (SN) , 2016, DOI: 10.4236/sn.2016.51002
Abstract: Community detection is one of the important tasks of social network analysis. It has significant practical importance for achieving cost-effective solutions for problems in the area of search engine optimization, spam detection, viral marketing, counter-terrorism, epidemic modeling, etc. In recent years, there has been an exponential growth of online social platforms such as Twitter, Facebook, Google+, Pinterest and Tumblr, as people can easily connect to each other in the Internet era overcoming geographical barriers. This has brought about new forms of social interaction, dialogue, exchange and collaboration across diverse social networks of unprecedented scales. At the same time, it presents new challenges and demands more effective, as well as scalable, graphmining techniques because the extraction of novel and useful knowledge from massive amount of graph data holds the key to the analysis of social networks in a much larger scale. In this research paper, the problem to find communities within social networks is considered. Existing community detection techniques utilize the topological structure of the social network, but a proper combination of the available attribute data, which represents the properties of the participants or actors, and the structure data of the social network graph is promising for the detection of more accurate and meaningful communities.
Scalable Varied Density Clustering Algorithm for Large Datasets  [PDF]
Ahmed Fahim, Abd-Elbadeeh Salem, Fawzy Torkey, Mohamed Ramadan, Gunter Saake
Journal of Software Engineering and Applications (JSEA) , 2010, DOI: 10.4236/jsea.2010.36069
Abstract: Finding clusters in data is a challenging problem especially when the clusters are being of widely varied shapes, sizes, and densities. Herein a new scalable clustering technique which addresses all these issues is proposed. In data mining, the purpose of data clustering is to identify useful patterns in the underlying dataset. Within the last several years, many clustering algorithms have been proposed in this area of research. Among all these proposed methods, density clustering methods are the most important due to their high ability to detect arbitrary shaped clusters. Moreover these methods often show good noise-handling capabilities, where clusters are defined as regions of typical densities separated by low or no density regions. In this paper, we aim at enhancing the well-known algorithm DBSCAN, to make it scalable and able to discover clusters from uneven datasets in which clusters are regions of homogenous densities. We achieved the scalability of the proposed algorithm by using the k-means algorithm to get initial partition of the dataset, applying the enhanced DBSCAN on each partition, and then using a merging process to get the actual natural number of clusters in the underlying dataset. This means the proposed algorithm consists of three stages. Experimental results using synthetic datasets show that the proposed clustering algorithm is faster and more scalable than the enhanced DBSCAN counterpart.
Clusters Merging Method for Short Texts Clustering  [PDF]
Yu Wang, Lihui Wu, Hongyu Shao
Open Journal of Social Sciences (JSS) , 2014, DOI: 10.4236/jss.2014.29032
Abstract:

Under push of Mobile Internet, new social media such as microblog, we chat, question answering systems are constantly emerging. They produce huge amounts of short texts which bring forward new challenges to text clustering. In response to the features of large amount and dynamic growth of short texts, a two-stage clustering method was putted forward. This method adopted a sliding window sliding on the flow of short texts. Inside the slide window, hierarchical clustering method was used, and between the slide windows, clusters merging method based on information gain was adopted. Experiment indicated that this method is fast and has a higher accuracy.

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