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80 results for “Clustering Performance”
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Research ArticleOpen accessIn the abstractCluster Based Architecture for Cognitive Radio NetworkManoj Ramachandran, Harish Sheeranalli Venkatarama, Sandesh HebbarCatastrophic events like flood often result in the derangement of the communication networks. The consequences include difficulty in locating the survivors of the disaster. The purpose of this paper is to give an insight to the design and development of a Cognitive Radio Network that can be used as an immediate remedy communication network which can help in such situations. … …Journal of Computer Science, Vol. 15, Issue 1 · 12 Dec 2018 · 7,711 accessesView PDFCite
Research ArticleOpen accessIn the abstractAn Elite Pool-Based Big Bang-Big Crunch Metaheuristic for Data ClusteringIbrahim Al-Marashdeh, Ghaith M. Jaradat, Masri Ayob et al.This paper delves into the capacity of enhanced Big Bang-Big Crunch (EBB-BC) metaheuristic to handle data clustering problems. BB-BC is a product of an evolution theory of the universe in physics and astronomy. Two main phases of BB-BC are big bang and big crunch. The big bang phase involves a creation of a population of random initial solutions, while in … …Journal of Computer Science, Vol. 14, Issue 12 · 19 Jun 2018 · 7,879 accessesView PDFCite
InvestigationOpen accessIn the abstractA Conceptualization of Distributed Computation for Machine Learning: The Voting AlgorithmTalal Talib JameelThis paper describes a voting algorithm that can be used to find the most optimal solution to clustering problems in machine learning. As part of the family of algorithms known as Condorcet methods, the voting algorithm is used to choose a particular candidate, even in the absence of a definitive majority. The algorithm proceeds in two steps: Renormalization and reconciliation. … …American Journal of Engineering and Applied Sciences, Vol. 10, Issue 1 · 2 Mar 2017 · 7,890 accessesView PDFCite
Research ArticleOpen accessIn the abstractFractional Lion Algorithm-An Optimization Algorithm for Data ClusteringSatish Chander, P. Vijaya, Praveen DhyaniClustering divides the data available as bulk into meaningful, useful groups (Clusters) without any prior knowledge about the data. Cluster analysis provides an abstraction from individual data objects to the clusters in which those objects reside. It is a key technique in the data mining and has become an important issue in many fields. This paper presents a novel Fractional … …Journal of Computer Science, Vol. 12, Issue 7 · 12 Aug 2016 · 7,673 accessesView PDFCite
Research ArticleOpen accessIn the abstractClassification of Breast Tumor in Mammogram Images Using Unsupervised Feature LearningAidarus M. Ibrahim, Baharum Baharudin, Abas Md Said, P. N. HashimahIn this study, we propose a learning-based approach using feature learning to minimize the manual effort required to extract features. Firstly, we extracted features from equally spaced sub-patches covering the input Region of Interest (ROI). The dimensionality of the extracted features is reduced using max-pooling. Furthermore, spherical k-means clustering coupled with max pooling (k-means-max pooling) is compared with well-known feature … …American Journal of Applied Sciences, Vol. 13, Issue 5 · 13 May 2016 · 7,497 accessesView PDFCite
Literature ReviewOpen accessIn the abstractAdvances in Document Clustering with Evolutionary-Based AlgorithmsSarmad Makki, Razali Yaakob, Norwati Mustapha, Hamidah IbrahimDocument clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of … …American Journal of Applied Sciences, Vol. 12, Issue 10 · 20 Oct 2015 · 7,186 accessesView PDFCite
Case ReportOpen accessIn the abstractLoad Balancing Technique Based on Broadcasting and Residual Energy Using Adaptive Clustering Hierarchy Protocol (LBEACH)Abdullah Soliman Alshraa, Venus Wazeer SamawiOne of the important factors of Wireless Sensor Networks (WSNs) is sensors lifetime. Elongating sensors lifetime increases the benefits from the network capabilities as much as possible. To elongate WSNs lifetime, power consumption should be reduced. To do so, load balance technique is applied to distribute the energy consumption among cluster’s nodes in the WSN. In each cluster, the node … …Journal of Computer Science, Vol. 11, Issue 6 · 13 Oct 2015 · 6,610 accessesView PDFCite
Research ArticleOpen accessIn the abstractA Novel Unique Node Based Clustering and Location-Key Pair Based Security for Wireless NetworksT. R. Vedhavathy, M. S.K. ManikandanWireless ad hoc networks are used in emergency situations especially for surveillance and monitoring. The nodes in the ad hoc networks are self-organized and can accommodate by themselves with the available resources. The nodes are capable of random movement and hence there is no fixed infrastructure. The purpose of the ad hoc networks can be fulfilled only if all nodes … …Journal of Computer Science, Vol. 11, Issue 3 · 23 Apr 2015 · 6,430 accessesView PDFCite
EditorialOpen accessIn the abstractData Mining in Time Series: Current Study and Future TrendTan Yan, Liudmila Ulanova, Ye Ouyang, Fengyuan XuTime series represent sequences of data points where usually their order is defined by the time when they were recorded. Thus, virtually any sequential recordings can be stored as time series: Stock prices, weather conditions, physical system parameters change, product quality monitoring, etc. This leads to ubiquity of time series in all scientific and practical fields (Esling and Agon, 2012); … …Journal of Computer Science, Vol. 10, Issue 12 · 26 Jan 2015 · 8,756 accessesView PDFCite
Research ArticleOpen accessIn the abstractFROM DATA MINING AND KNOWLEDGE DISCOVERY TO BIG DATA ANALYTICS AND KNOWLEDGE EXTRACTION FOR APPLICATIONS IN SCIENCESubana Shanmuganathan"Data mining" for "knowledge discovery in databases" and associated computational operations first introduced in the mid-1990 s can no longer cope with the analytical issues relating to the so-called "big data". The recent buzzword big data refers to large volumes of diverse, dynamic, complex, longitudinal and/or distributed data generated from instruments, sensors, Internet transactions, email, video, click streams, noisy, structured/unstructured … …Journal of Computer Science, Vol. 10, Issue 12 · 20 Jan 2015 · 8,740 accessesView PDFCite