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ant clustering algorithm with K-harmonic means clustering

EXPERT SYSTEMS WITH APPLICATIONS 2010年第12期37卷 8679-8684页

作者： Jiang, Hua Yi, Shenghe Li, Jing Yang, Fengqin Hu, Xin NE Normal Univ Coll Comp Sci Changchun 130117 Jilin Peoples R China

clustering is an unsupervised learning procedure and there is no a prior knowledge of data distribution. It organizes a set of objects/data into similar groups called clusters, and the objects within one cluster are highly similar and dissimilar with the objects in other clusters. The classic K-means algorithm (KM) is the most popular clustering algorithm for its easy implementation and fast working. But KM is very sensitive to initialization, the better centers we choose, the better results we get. Also, it is easily trapped in local optimal. The K-harmonic means algorithm (KHM) is less sensitive to the initialization than the KM algorithm. The ant clustering algorithm (ACA) can avoid trapping in local optimal solution. In this paper, we will propose a new clustering algorithm using the ant clustering algorithm with K-harmonic means clustering (ACAKHM). The experiment results on three well-known data sets like Iris and two other artificial data sets indicate the superiority of the ACAKHM algorithm. At last the performance of the ACAKHM algorithm is compared with the ACA and the KHM algorithm. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： clustering K-means K-harmonic means clustering ant clustering algorithm

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ant clustering algorithm with Information Theoretic Learning 8th

Ant Clustering Algorithm with Information Theoretic Learning

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8th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Boryczka, Urszula Boryczka, Mariusz Univ Silesia Inst Comp Sci Sosnowiec Poland

ISBN: (纸本)9783319396309;9783319396293

In this paper a novel ant clustering algorithm with information theoretic learning consolidation is presented. Derivation of the information potential and its force from Renyi's entropy have been used to create an interesting model of ant's movement during clusterization process. In this approach each object is treated as a single agent-ant. What is more, in a local environment each agent-ant moves in accordance to information forces influence. The outcome of all information forces determines the direction and range of agent-ants' movement. Stopping criterion used in this approach indirectly emerges from Renyi entropy. This modified algorithm has been tested on different data sets and comparative study shows the effectiveness of the proposed clustering algorithm.

关键词： ant clustering algorithm Data mining Information theoretic learning

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Application of K-means algorithm based on ant clustering algorithm in macroscopic planning of highway transportation hub

Application of K-means algorithm based on ant clustering alg...

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1st International Symposium on Information Technologies and Applications in Education (ISITAE 2007)

作者： Meng, Yan Liu, Xiyu Shandong Normal Univ Dept Management & Econ Jinan 250014 Peoples R China

ISBN: (纸本)9781424413850

Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely rued algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of K-means algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than K-means algorithm and ant clustering algorithm.

关键词： ant clustering algorithm highway transportation K-means algorithm main hub cities

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The Implementation of ant clustering algorithm (ACA) in clustering and Classifying the Tropical Wood Species

The Implementation of Ant Clustering Algorithm (ACA) in Clus...

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9th International Conference on Signal-Image Technology and Internet-Based Systems (SITIS)

作者： Ahmad, Azlin Yusof, Rubiyah Univ Teknol MARA UiTM Fac Comp & Math Sci Shah Alam Malaysia Univ Teknol Malaysia CAIRO MJIIT Kuala Lumpur Malaysia

ISBN: (纸本)9781479932115

The ant clustering algorithm (ACA) is a biological inspired data clustering technique;which aimed to cluster and classify the data patterns into different groups. This paper shows how the ant clustering algorithm (ACA) is implemented in clustering and classifying the tropical wood species. As for feature extraction in this research, two feature extractors are selected to extract wood features from wood images;which are Basic Grey Level Aura Matrices (BGLAM) and Statistical Properties of Pores Distribution (SPPD). The ACA algorithm is then been applied in wood data training and testing, and as a result, it is proven that the ACA algorithm can cluster and classify the tropical wood data accurately and effectively.

关键词： ant clustering algorithm clustering Tropical Wood Species

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Adaptive ant clustering algorithm with Pheromone 8th

Adaptive Ant Clustering Algorithm with Pheromone

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8th Asian Conference on Intelligent Information and Database Systems (ACIIDS)

作者： Boryczka, Urszula Kozak, Jan Univ Silesia Inst Comp Sci Sosnowiec Poland

ISBN: (纸本)9783662493908;9783662493892

In the midst of data mining tasks, clustering algorithms received special attention, especially when these techniques are bio-inspired and while they use special methods which improve a learning process during clusterization. Most promising among them are ant-based approaches. The process of clustering with colony of virtual ants is emerging and can be an alternative, when the data is complicated. clustering, based on ant's behavior, was first introduced by Deneubourg et al. in 1991 and this classical proposition still requires investigation to improve stability, scalability and convergence of speed. This investigations will show that we can create a mature tool for clustering. The aim of this research was to examine the execution of a new ant clustering algorithm with a modified scheme of ants' perception and an incorporation of pheromone matrices. To assess the performance of our proposition, certain amount of widely known benchmark data sets were used. Empirical study of our approach shows that the adACA performs well when the pheromone matrices influence the behavior of clustering ants and leads to better results.

关键词： Data mining Cluster analysis ant clustering algorithm

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Analysis on the Human Errors in Coal Mines based on ant clustering algorithm

Analysis on the Human Errors in Coal Mines based on Ant Clus...

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3rd International Conference on Green Power, Materials and Manufacturing Technology and Applications

作者： Liu, Shuangyue Fang, Zixin Univ Sci & Technol Beijing Civil & Environm Engn Sch Beijing 100083 Peoples R China

ISBN: (纸本)9783037859865

The human error mechanism in coal mine safety is analyzed specifically from safety psychological and physiological factors, worker's quality, safety management, safety education, mechanical equipment, and working environment, and also a human error' dominant factors classification model playing a great effect on the safety production of coal mine is established with the application of ant clustering algorithm. The experimental results show that management is the key in the human errors of coal mine

关键词： ant clustering algorithm Coal Mine Human Errors

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Application of K-means algorithm Based on ant clustering algorithm in Macroscopic Planning of Highway Transportation Hub

Application of K-means Algorithm Based on Ant Clustering Alg...

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第一届国际教育信息技术及应用研讨会(2007 1st International Symposium on Information Technologies and Applications in Education(ISITAE 2007))

作者： Yan Meng Xiyu Liu Department of Management and Economy Shandong Normal University Jinan 250014 China

Development of highway transportation promotes sustainable and rapid development in economy of our country effectively. But construction of highway and transportation hub shows the nature of unbalance. So highway main hub cities must be clustered using cluster analysis, and then divided level in order to functional analyze. K-means algorithm is the most widely used algorithm in clustering analysis, which clustering numbers and initial clustering centers are uncertain. This paper proposes application of Kmeans algorithm in macroscopic planning of highway transportation hub based on ant clustering algorithm. The experimental results show this algorithm can more effectively solved clustering problem than Kmeans algorithm and ant clustering algorithm.

关键词： ant clustering algorithm Highway Transportation K-means algorithm Main Hub Cities

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Analysis on the Human Errors in Coal Mines based on ant clustering algorithm

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International Journal of Technology Management 2013年第1期 61-63页

作者： Shuangyue LIU Zixin FANG Civil and Environmental Engineering School University of Science and Technology Beijing Beijing 100083 China

The human error mechanism in coal mine safety is analyzed specifically from safety psychological and physiological factors, worker＇ s quality, safety management, safety education, mechanical equipment, and working environment, and also a human error＇ dominant factors classification model playing a great effect on the safety production of coal mine is established with the application of ant clustering algorithm. The experimental results show that management is the key in the human errors of coal mine.

关键词： ant clustering algorithm Coal Mine Human Errors

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A new hybrid method based on partitioning-based DBSCAN and ant clustering

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第8期38卷 9373-9381页

作者： Jiang, Hua Li, Jing Yi, Shenghe Wang, Xiangyang Hu, Xin NE Normal Univ Coll Comp Sci Changchun 130117 Jilin Peoples R China

clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. It can discover clusters with arbitrary shapes and only requires two input parameters. DBSCAN has been proved to be very effective for analyzing large and complex spatial databases. However, DBSCAN needs large volume of memory support and often has difficulties with high-dimensional data and clusters of very different densities. So, partitioning-based DBSCAN algorithm (PDBSCAN) was proposed to solve these problems. But PDBSCAN will get poor result when the density of data is non-uniform. Meanwhile, to some extent. DBSCAN and PDBSCAN are both sensitive to the initial parameters. In this paper, we propose a new hybrid algorithm based on PDBSCAN. We use modified ant clustering algorithm (ACA) and design a new partitioning algorithm based on 'point density' (PD) in data preprocessing phase. We name the new hybrid algorithm PACA-DBSCAN. The performance of PACA-DBSCAN is compared with DBSCAN and PDBSCAN on five data sets. Experimental results indicate the superiority of PACA-DBSCAN algorithm. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： clustering Density-based clustering DBSCAN PDBSCAN ant clustering algorithm

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Hybrid ant Swarm-Based Data clustering

Hybrid Ant Swarm-Based Data Clustering

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IEEE World AI IoT Congress (AIIoT)

作者： Azam, Md Ali Hossen, Md Abir Rahman, Md Hafizur South Dakota Sch Mines & Technol Elect Engn Rapid City SD 57701 USA

ISBN: (纸本)9781665435680

Biologically inspired computing techniques are very effective and useful in many areas of research including data clustering. ant clustering algorithm is a nature-inspired clustering technique which is extensively studied for over two decades. In this study, we extend the ant clustering algorithm (ACA) to a hybrid ant clustering algorithm (hACA). Specifically, we include a genetic algorithm in standard ACA to extend the hybrid algorithm for better performance. We also introduced novel pick up and drop off rules to speed up the clustering performance. We study the performance of the hACA algorithm and compare with standard ACA as a benchmark.

关键词： Swarm intelligence data clustering ant clustering algorithm genetic algorithm

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