An Alternative Clustering Scheme in WSN

2015 ◽  
Vol 15 (7) ◽  
pp. 4148-4155 ◽  
Author(s):  
Davood Izadi ◽  
Jemal Abawajy ◽  
Sara Ghanavati
Keyword(s):  
Alternative Clustering

Alternative Clustering

2017 ◽  
pp. 1-12 ◽  
Author(s):  
Avinash Navlani ◽  
V. B. Gupta
Keyword(s):  
Research Problem ◽  
Research Community ◽  
Data Sets ◽  
Complex Data ◽  
High Quality ◽  
Data Set ◽  
Alternative Clustering ◽  
Complex Data Sets ◽  
Data Objects ◽  
Community Clustering

In the last couple of decades, clustering has become a very crucial research problem in the data mining research community. Clustering refers to the partitioning of data objects such as records and documents into groups or clusters of similar characteristics. Clustering is unsupervised learning, because of unsupervised nature there is no unique solution for all problems. Most of the time complex data sets require explanation in multiple clustering sets. All the Traditional clustering approaches generate single clustering. There is more than one pattern in a dataset; each of patterns can be interesting in from different perspectives. Alternative clustering intends to find all unlike groupings of the data set such that each grouping has high quality and distinct from each other. This chapter gives you an overall view of alternative clustering; it's various approaches, related work, comparing with various confusing related terms like subspace, multi-view, and ensemble clustering, applications, issues, and challenges.

scholarly journals Community structure in the World Trade Network based on communicability distances

2020 ◽  
Author(s):  
Paolo Bartesaghi ◽  
Gian Paolo Clemente ◽  
Rosanna Grassi
Keyword(s):  
Community Structure ◽  
Numerical Analysis ◽  
World Trade ◽  
Metric Spaces ◽  
Trade Network ◽  
Distance Threshold ◽  
Alternative Clustering ◽  
Mesoscale Structure ◽  
Long Range Interactions ◽  
The World

AbstractIn this paper, we investigate the mesoscale structure of the World Trade Network. In this framework, a specific role is assumed by short- and long-range interactions, and hence by any suitably defined network-based distance between countries. Therefore, we identify clusters through a new procedure that exploits Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The proposed methodology aims at finding the distance threshold that maximizes a specific quality function defined for general metric spaces. Main advantages regard the computational efficiency of the procedure as well as the possibility to inspect intercluster and intracluster properties of the resulting communities. The numerical analysis highlights peculiar relationships between countries and provides a rich set of information that can hardly be achieved within alternative clustering approaches.

An alternative clustering approach for reconstructing cross cut shredded text documents

2011 ◽  
Vol 52 (3) ◽  
pp. 1491-1501 ◽  
Author(s):  
Azzam Sleit ◽  
Yacoub Massad ◽  
Mohammed Musaddaq
Keyword(s):  
Text Documents ◽  
Alternative Clustering ◽  
Clustering Approach

Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables

Psychometrika ◽  
1984 ◽  
Vol 49 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Wayne S. DeSarbo ◽  
J. Douglas Carroll ◽  
Linda A. Clark ◽  
Paul E. Green
Keyword(s):  
Alternative Clustering ◽  
Differential Weighting

scholarly journals Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

2021 ◽  
Vol 18 (2(Suppl.)) ◽  
pp. 1001
Author(s):  
Jojo Blanza
Keyword(s):  
Spectral Clustering ◽  
Channel Model ◽  
Channel Modeling ◽  
Jaccard Index ◽  
Affinity Matrix ◽  
Number Of Clusters ◽  
Alternative Clustering ◽  
Directional Cosine ◽  
Indoor Scenarios ◽  
The Cost

This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.

The Kernel Rough K-Means Algorithm

2020 ◽  
Vol 13 (2) ◽  
pp. 234-239
Author(s):  
Wang Meng ◽  
Dui Hongyan ◽  
Zhou Shiyuan ◽  
Dong Zhankui ◽  
Wu Zige
Keyword(s):  
Clustering Algorithm ◽  
Rough Set Theory ◽  
Fuzzy Theory ◽  
Clustering Algorithms ◽  
Attractive Alternative ◽  
Complex Data ◽  
Soft Clustering ◽  
Alternative Clustering ◽  
Clustering And Classification ◽  
Hard Clustering

Background: Clustering is one of the most important data mining methods. The k-means (c-means ) and its derivative methods are the hotspot in the field of clustering research in recent years. The clustering method can be divided into two categories according to the uncertainty, which are hard clustering and soft clustering. The Hard C-Means clustering (HCM) belongs to hard clustering while the Fuzzy C-Means clustering (FCM) belongs to soft clustering in the field of k-means clustering research respectively. The linearly separable problem is a big challenge to clustering and classification algorithm and further improvement is required in big data era. Objective: RKM algorithm based on fuzzy roughness is also a hot topic in current research. The rough set theory and the fuzzy theory are powerful tools for depicting uncertainty, which are the same in essence. Therefore, RKM can be kernelized by the mean of KFCM. In this paper, we put forward a Kernel Rough K-Means algorithm (KRKM) for RKM to solve nonlinear problem for RKM. KRKM expanded the ability of processing complex data of RKM and solve the problem of the soft clustering uncertainty. Methods: This paper proposed the process of the Kernel Rough K-Means algorithm (KRKM). Then the clustering accuracy was contrasted by utilizing the data sets from UCI repository. The experiment results shown the KRKM with improved clustering accuracy, comparing with the RKM algorithm. Results: The classification precision of KFCM and KRKM were improved. For the classification precision, KRKM was slightly higher than KFCM, indicating that KRKM was also an attractive alternative clustering algorithm and had good clustering effect when dealing with nonlinear clustering. Conclusion: Through the comparison with the precision of KFCM algorithm, it was found that KRKM had slight advantages in clustering accuracy. KRKM was one of the effective clustering algorithms that can be selected in nonlinear clustering.

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