scholarly journals A simulated annealing-based maximum-margin clustering algorithm

2018 ◽  
Vol 35 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Sattar Seifollahi ◽  
Adil Bagirov ◽  
Ehsan Zare Borzeshi ◽  
Massimo Piccardi
Keyword(s):  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Maximum Margin Clustering ◽  
Maximum Margin

A maximum margin clustering algorithm based on indefinite kernels

2019 ◽  
Vol 13 (4) ◽  
pp. 813-827
Author(s):  
Hui Xue ◽  
Sen Li ◽  
Xiaohong Chen ◽  
Yunyun Wang
Keyword(s):  
Clustering Algorithm ◽  
Maximum Margin Clustering ◽  
Maximum Margin

scholarly journals Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

2016 ◽  
Vol 16 (6) ◽  
pp. 27-42 ◽  
Author(s):  
Minghan Yang ◽  
Xuedong Gao ◽  
Ling Li
Keyword(s):  
Simulated Annealing ◽  
Data Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Parameter Determination ◽  
Data Sets ◽  
Parameter Vector ◽  
Clustering Validity

Abstract Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

CLUSTERING USING SIMULATED ANNEALING WITH PROBABILISTIC REDISTRIBUTION

2001 ◽  
Vol 15 (02) ◽  
pp. 269-285 ◽  
Author(s):  
SANGHAMITRA BANDYOPADHYAY ◽  
UJJWAL MAULIK ◽  
MALAY KUMAR PAKHIRA
Keyword(s):  
Simulated Annealing ◽  
Clustering Algorithm ◽  
Minimum Energy ◽  
Real Life ◽  
Feature Space ◽  
Cluster Center ◽  
Data Sets ◽  
Partitional Clustering ◽  
Real Life Data ◽  
Data Points

An efficient partitional clustering technique, called SAKM-clustering, that integrates the power of simulated annealing for obtaining minimum energy configuration, and the searching capability of K-means algorithm is proposed in this article. The clustering methodology is used to search for appropriate clusters in multidimensional feature space such that a similarity metric of the resulting clusters is optimized. Data points are redistributed among the clusters probabilistically, so that points that are farther away from the cluster center have higher probabilities of migrating to other clusters than those which are closer to it. The superiority of the SAKM-clustering algorithm over the widely used K-means algorithm is extensively demonstrated for artificial and real life data sets.

scholarly journals Energy Efficiency Optimization for Intermediate Node Selection Using MhSA-LEACH: Multi-hop Simulated Annealing in Wireless Sensor Network

2020 ◽  
Vol 8 (1) ◽  
pp. 1-18
Author(s):  
Aidil Saputra Kirsan ◽  
Udin Harun Al Rasyid ◽  
Iwan Syarif ◽  
Dian Neipa Purnamasari
Keyword(s):  
Simulated Annealing ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Information Exchange ◽  
Clustering Algorithm ◽  
Technological Development ◽  
Base Station ◽  
Wireless Sensor ◽  
Intermediate Node ◽  
Leach Protocol

Energy usage on nodes is still a hot topic among researchers on wireless sensor networks. This is due to the increasing technological development increasing information requirements and caused the occurrence of information exchange continuously without stopping and impact the decline of lifetime nodes. It takes more effort to manually change the energy source on nodes in the wireless sensor network. The solution to such problems is to use routing protocols such as Low Energy Adaptive Clustering Hierarchy (LEACH). The LEACH protocol works by grouping nodes and selecting the Cluster Head (CH) in charge of delivering data to the Base Station (BS). One of the disadvantage LEACH protocols, when nodes are far from the CH, will require a lot of energy for sending data to CH. One way to reduce the energy consumption of each node-far is to use multi-hop communication. In this research, we propose a multi-hop simulated annealing (MhSA-LEACH) with an algorithm developed from the LEACH protocol based on intra-cluster multi-hop communication. The selection of intermediate nodes in multi-hop protocol is done using Simulated Annealing (SA) algorithm on Traveling Salesman Problem (TSP). Therefore, the multi-hop nodes are selected based on the shortest distance and can only be skipped once by utilizing the probability theory, resulting in a more optimal node path. The proposed algorithm has been compared to the conventional LEACH protocol and the Multi-Hop Advance Heterogeneity-aware Energy Efficient (MAHEE) clustering algorithm using OMNeT++. The test results show the optimization of MhSA-LEACH on the number of packets received by BS or CH and the number of dead or alive nodes from LEACH and MAHEE protocols.

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