A Revised Ant Clustering Algorithm with Obstacle Constraints
Clustering of spatial data in the presence of obstacles has a wide application. It is an important research topic in the spatial data mining. This paper discusses the problem of spatial clustering with obstacles constraints and presents a revised method named ant clustering algorithm with obstacle c...
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| Vydáno v: | 2009 WRI World Congress on Computer Science and Information Engineering : March 31, 2009-April 2, 2009 Ročník 3; s. 679 - 683 |
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| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
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IEEE
01.03.2009
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| ISBN: | 9780769535074, 0769535070 |
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| Abstract | Clustering of spatial data in the presence of obstacles has a wide application. It is an important research topic in the spatial data mining. This paper discusses the problem of spatial clustering with obstacles constraints and presents a revised method named ant clustering algorithm with obstacle constraints(ACAOC) based on the basic ant model. This algorithm avoids some defects of other spatial clustering algorithms. These defects make algorithm not iterate when it has arrived at the stagnating state of the iteration or local optimum solution. ACAOC algorithm proposed in this paper cannot only give attention to local converging and the whole converging, but also consider the obstacles that exit in the real world and make the clustering result more practical. Because of the use of approximate nearest neighbor (ANN), the computing speed is increased greatly. The last experimental results conducted on synthetic data sets demonstrate that this method could extract the correct number of clusters with good clustering quality and high whole converging speed compared to the results obtained from clustering algorithm ignoring considering obstacles constraints. |
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| AbstractList | Clustering of spatial data in the presence of obstacles has a wide application. It is an important research topic in the spatial data mining. This paper discusses the problem of spatial clustering with obstacles constraints and presents a revised method named ant clustering algorithm with obstacle constraints(ACAOC) based on the basic ant model. This algorithm avoids some defects of other spatial clustering algorithms. These defects make algorithm not iterate when it has arrived at the stagnating state of the iteration or local optimum solution. ACAOC algorithm proposed in this paper cannot only give attention to local converging and the whole converging, but also consider the obstacles that exit in the real world and make the clustering result more practical. Because of the use of approximate nearest neighbor (ANN), the computing speed is increased greatly. The last experimental results conducted on synthetic data sets demonstrate that this method could extract the correct number of clusters with good clustering quality and high whole converging speed compared to the results obtained from clustering algorithm ignoring considering obstacles constraints. |
| Author | Xiyu Liu Jianhua Qu |
| Author_xml | – sequence: 1 surname: Jianhua Qu fullname: Jianhua Qu organization: Sch. of Manage., Shandong Normal Univ., Jinan, China – sequence: 2 surname: Xiyu Liu fullname: Xiyu Liu organization: Sch. of Manage., Shandong Normal Univ., Jinan, China |
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| Snippet | Clustering of spatial data in the presence of obstacles has a wide application. It is an important research topic in the spatial data mining. This paper... |
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| SubjectTerms | ANN Ant algorithm Application software Bridges Buildings Clustering algorithms Computer science data clustering Data engineering Data mining Engineering management Nearest neighbor searches Obstacle constraints Rivers |
| Title | A Revised Ant Clustering Algorithm with Obstacle Constraints |
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