Location-Based Mobile Community Using Ants-Based Cluster Algorithm
| Main Author: | Srisa-an, Chetneti |
|---|---|
| Format: | Article info application/pdf eJournal |
| Bahasa: | eng |
| Terbitan: |
Bright Institute
, 2021
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| Subjects: | |
| Online Access: |
http://ijaim.net/journal/index.php/ijaim/article/view/6 http://ijaim.net/journal/index.php/ijaim/article/view/6/6 |
| ctrlnum |
article-6 |
|---|---|
| fullrecord |
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<dc schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><title lang="en-US">Location-Based Mobile Community Using Ants-Based Cluster Algorithm</title><creator>Srisa-an, Chetneti</creator><subject lang="en-US">Mobile location based service</subject><subject lang="en-US">WebMining</subject><subject lang="en-US">Ant-based clustering</subject><description lang="en-US">A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA &amp; VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters.</description><publisher lang="en-US">Bright Institute</publisher><date>2021-04-25</date><type>Journal:Article</type><type>Other:info:eu-repo/semantics/publishedVersion</type><type>Journal:Article</type><type>File:application/pdf</type><identifier>http://ijaim.net/journal/index.php/ijaim/article/view/6</identifier><identifier>10.47738/ijaim.v1i1.6</identifier><source lang="en-US">International Journal for Applied Information Management; Vol. 1 No. 1 (2021): Regular Issue: April 2021; 36-41</source><source>10.47738/ijaim.v1i1</source><language>eng</language><relation>http://ijaim.net/journal/index.php/ijaim/article/view/6/6</relation><rights lang="en-US">Copyright (c) 2021 International Journal for Information Management</rights><rights lang="en-US">https://creativecommons.org/licenses/by-sa/4.0</rights><recordID>article-6</recordID></dc>
|
| language |
eng |
| format |
Journal:Article Journal Other:info:eu-repo/semantics/publishedVersion Other File:application/pdf File Journal:eJournal |
| author |
Srisa-an, Chetneti |
| title |
Location-Based Mobile Community Using Ants-Based Cluster Algorithm |
| publisher |
Bright Institute |
| publishDate |
2021 |
| topic |
Mobile location based service WebMining Ant-based clustering |
| url |
http://ijaim.net/journal/index.php/ijaim/article/view/6 http://ijaim.net/journal/index.php/ijaim/article/view/6/6 |
| contents |
A location based service (LBS) is widely used on modern smartphone around the world asits built-in features. Each smartphone can access a google API or map. People can therefore share their location (latitude and longitude) among friends. Many LBS spots can easily form “location based mobile community (LBMC).” Since the nodes are mobile, the community group changes dynamically and is unstructured. Ant-based clustering algorithm is a special kind of optimization technique which is highly suitable for finding the adaptive clustering for volatile networks. This Paper Aims To form a location based mobile community (LBMC) by using Ant-based clustering algorithm. Due to the mobile type community, a vanishing community problem is also stated in this paper. Instead of redo a whole algorithm again, we modify an original algorithm by applying a pheromone concept to handle a change. Our algorithm is named as ABCA & VP which stands for Ant-Based Clustering Algorithm with Vanishing problem. More than 5,000 samples from their latitude and longitude coordinates in Thailand. From an experiment, K-means clustering work well in small data size and low number of clusters. In Small size of data between 50 and 1000, our algorithm runs battery when a number of clusters reach 15 clusters. In a big data size (between 1,000 and 5,000 samples), our algorithm outperforms K-means clustering when a number of clusters reach 20 clusters. |
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Library and Information Sciences/Perpustakaan dan Ilmu Informasi Information Management/Manajemen Informasi |
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JAWA TENGAH |
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