K-Centers Dynamic Clustering Algorithms and Applications
| Main Author: | |
|---|---|
| Language: | English |
| Published: |
University of Cincinnati / OhioLINK
2013
|
| Subjects: | |
| Online Access: | http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644 |
| id |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin1384427644 |
|---|---|
| record_format |
oai_dc |
| spelling |
ndltd-OhioLink-oai-etd.ohiolink.edu-ucin13844276442021-08-03T06:20:21Z K-Centers Dynamic Clustering Algorithms and Applications Xie, Qing Yan Computer Science Clustering algorithm K-Means K-Centers Min-Max clustering algorithm mean-shift K-Centers Mean-shift Reverse Mean-shift wireless sensor networks Every day large and increasing amounts of unstructured information are created, putting ever more demands on retrieval methods, classification, automatic data analysis and management. Clustering is an important and efficient way for organizing and analyzing information and data. One of the most widely used dynamic clustering algorithms is K-Means clustering. This dissertation presents our K-Centers Min-Max dynamic clustering algorithm (KCMM) and K-Centers Mean-shift Reverse Mean-shift dynamic clustering algorithm (KCMRM). These algorithms are designed to modify K-Means in order to achieve improved performance and help with specific goals in certain domains. These two algorithms can be applied to many fields such as wireless sensor networks, server or facility location optimization, and molecular networks. Their application in wireless sensor networks are described in this dissertation. The K-Centers Min-Max clustering algorithm uses a smallest enclosing disk/sphere algorithm to attain a minimum of the maximum distance between a cluster node and data nodes. Our approach results in fewer iterations, and shorter maximum intra-cluster distances than the standard K-Means clustering algorithm with either uniform distribution or normal distribution. Most notably, it can achieve much better performance when the size of clusters is large, or when the clusters includes large numbers of member nodes in normal distribution.The K-Centers Mean-shift Reverse Mean-shift clustering algorithm is proposed to solve the "empty cluster" problem which is caused by random deployment. It employs a Gaussian function as a kernel function, discovers the relationship between mean shift and gradient ascent on the estimated density surface, and iteratively moves cluster nodes away from their weighted means. This results in cluster nodes which better accommodate the distribution of data nodes. The K-Centers Mean-shift Reverse Mean-shift algorithm can not only reduce the number of empty clusters, but can also make the sizes of clusters are more evenly balanced compared to K-Means and K-Centers Min-Max clustering algorithms.In wireless sensor networks, addressing energy dissipation is a key issue. For heterogeneous wireless sensor networks, energy consumption to transmit data is proportional to the distance between sensor nodes and cluster heads or to a base station. Clustering is one of the best methods to reduce energy dissipation and extend network lifetimes. The K-Centers Min-Max and K-Centers Mean-shift Reverse Mean-shift clustering algorithms are applied to two proposed protocols, KCMM and KCMRM, for wireless sensor networks. Desirable features of the proposed clustering protocols KCMM and KCMRM include: energy efficiency; distributed and localized data aggregation; adaptation to changes in sensor distribution; robustness to partial damage; and self-recovery. Besides the above features, KCMRM protocol can make use of cluster heads efficiently and can reduce empty clusters. 2013 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws. |
| collection |
NDLTD |
| language |
English |
| sources |
NDLTD |
| topic |
Computer Science Clustering algorithm K-Means K-Centers Min-Max clustering algorithm mean-shift K-Centers Mean-shift Reverse Mean-shift wireless sensor networks |
| spellingShingle |
Computer Science Clustering algorithm K-Means K-Centers Min-Max clustering algorithm mean-shift K-Centers Mean-shift Reverse Mean-shift wireless sensor networks Xie, Qing Yan K-Centers Dynamic Clustering Algorithms and Applications |
| author |
Xie, Qing Yan |
| author_facet |
Xie, Qing Yan |
| author_sort |
Xie, Qing Yan |
| title |
K-Centers Dynamic Clustering Algorithms and Applications |
| title_short |
K-Centers Dynamic Clustering Algorithms and Applications |
| title_full |
K-Centers Dynamic Clustering Algorithms and Applications |
| title_fullStr |
K-Centers Dynamic Clustering Algorithms and Applications |
| title_full_unstemmed |
K-Centers Dynamic Clustering Algorithms and Applications |
| title_sort |
k-centers dynamic clustering algorithms and applications |
| publisher |
University of Cincinnati / OhioLINK |
| publishDate |
2013 |
| url |
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1384427644 |
| work_keys_str_mv |
AT xieqingyan kcentersdynamicclusteringalgorithmsandapplications |
| _version_ |
1719435140905566208 |