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Exploring the computational and impl...

Dai, Dajun.

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Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection./
作者:	Dai, Dajun.
面頁冊數:	150 p.
附註:	Adviser: Tonny J. Oyana.
Contained By:	Dissertation Abstracts International68-05A.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3264806
ISBN:	9780549034704

Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection.
Dai, Dajun.

Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection. - 150 p.

Adviser: Tonny J. Oyana.

Thesis (Ph.D.)--Southern Illinois University at Carbondale, 2007.

Cluster detection is an important exploratory data-mining tool in Geographic Information Science (GISci). Traditional cluster detection methods in GISci assume clusters to be in either circular or in horizontal and vertical elliptic shapes. However, these methods are limited and may not capture diagonally elongated clusters. Consequently, these methods often delineate clusters with low accuracy, take lengthy runtime, or at times fail to report clusters.

ISBN: 9780549034704Subjects--Topical Terms:

769149
Artificial Intelligence.

Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection.
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245 1 0 $a Exploring the computational and implementation characteristics of an improved genetic algorithm for cluster detection.
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500 $a Source: Dissertation Abstracts International, Volume: 68-05, Section: A, page: 2110.
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520 $a Cluster detection is an important exploratory data-mining tool in Geographic Information Science (GISci). Traditional cluster detection methods in GISci assume clusters to be in either circular or in horizontal and vertical elliptic shapes. However, these methods are limited and may not capture diagonally elongated clusters. Consequently, these methods often delineate clusters with low accuracy, take lengthy runtime, or at times fail to report clusters.
520 $a The goal of this dissertation is to develop an improved genetic algorithm (GA) with a new flexible gene structure for detecting clusters in both circular and elongated clusters in any direction. By introducing flexibly oriented ellipses as the gene structure to better represent diagonally elongated clusters, a candidate solution list to record all possible solutions, and a combination algorithm to merge overlapping solutions, the improved genetic algorithm in this research delineates cluster boundaries close to the real shapes of clusters. It also has a competent runtime and a high percentage rate of cluster detection. The performance test on both synthetic and real-world data sets with the comparison to PROgram for CLUster DEtection and Kulldorff's suggests that the improved genetic algorithm in this research can be a viable accurate, efficient, and reliable approach to cluster detection.
520 $a The combination of the improved genetic algorithm with an artificial neural network method, self-organizing maps (SOMs) shows that improved genetic algorithm can be effective and reliable in high-dimensional environmental data mining. Finally, using the improved genetic algorithm, combined with SOMs, Geographic Information Systems (GIS), spatial techniques and statistical methods, this research evaluates dioxin contamination in the city of Midland and the Tittabawassee River floodplain in Michigan and its impact on the prevalence rates of adult female breast cancer. The improved genetic algorithm delineates the clusters of breast cancer accurately and at the same time minimizes the inclusion of background population at risk.
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