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...
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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 clusteringalgorithm.
In the midst of data mining tasks, clusteringalgorithms received special attention, especially when these techniques are bio-inspired and while they use special methods which improve a learning process during cluster...
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ISBN:
(纸本)9783662493908;9783662493892
In the midst of data mining tasks, clusteringalgorithms 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 clusteringants and leads to better results.
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 ...
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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
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 stu...
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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.
clustering analysis is used in many disciplines and applications. This data mining task is an essential tool that identifies groups of objects based on similarity measures. The ant clustering algorithm is a swarm-inte...
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clustering analysis is used in many disciplines and applications. This data mining task is an essential tool that identifies groups of objects based on similarity measures. The ant clustering algorithm is a swarm-intelligent method used for clustering problems, that is inspired by the natural behavior of ants, which collect their corpses and sort their larvae. In recent years, many new metaheuristic algorithms have explored different scientific fields using inspirations or motivations in biological or nature-inspired derivations. The same situation is observed in area of data mining, where plenty of metaheuristics was presented as a new and effective approach. This state causes some difficulties or disappointments to have enough explanations or deep extent analysis to justify such applications. A new version of an ant clustering algorithm, using cellular automata mechanisms of transitions, is proposed to examine the computational efficiency and accuracy of the antclustering approach. Based on the similarity of two approaches arising from ant Sleeping Model, computational analysis of difficulties and complexities are presented and results show that the modification of an ant clustering algorithm - ASM MOD produces results that are not only more stable but also more efficiently determined than the prototype of ASM. The aim of this paper is not explicitly criticize these approaches but also to find a way of the repair these algorithms.
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...
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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.
Trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are tw...
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Trends in algorithm design have shown that hybrid algorithms, which combine or merge multiple algorithms, can create synergies to overcome the inherent limitations of the underlying individual algorithms. There are two broad types of hybridization: collaborative - individual algorithms tackle an instance of the problem sequentially or in parallel and exchange information accordingly while solving the problem; integrative - individual algorithms are dedicated to tackling different aspect(s) of the problem-solving process. In this thesis, we propose a schema for an enhanced form of integrative hybridization that blends granular algorithmic components from multiple algorithms to derive a new singular clusteringalgorithm. As a case study for the proposed hybridization technique, we examine ant clustering algorithm (a swarm intelligence algorithm that is based on the natural phenomenon of brood sorting in some species of ants); highlight the strengths and weaknesses of the algorithm; and present a blend of algorithmic components from tabu search into the algorithm to improve its solution quality. Empirical results from applying the blended algorithm to clustering benchmark datasets show improved clustering validation measures for the proposed blended hybrid algorithm compared to other forms of hybridization of the same underlying individual algorithms. Besides, the quality of clusters uncovered by this hybrid algorithm competes favorably with those uncovered using popular clusteringalgorithms such as DBSCAN and mean shift. Finally, we show the feasibility and viability of the blended algorithm when used in a novel application of clustering to the estimation of the cost of claims for group insurance benefits.
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 en...
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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.
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 h...
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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 clusteringalgorithm 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 clusteringalgorithm 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.
In this paper we deal with the combinatorial state space explosion problem which can occur in the generation of the state space in explicit model checking. The generation phase will determine the performances of the r...
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ISBN:
(纸本)9781467396691
In this paper we deal with the combinatorial state space explosion problem which can occur in the generation of the state space in explicit model checking. The generation phase will determine the performances of the rest model checking, which justify the need of efficient methods to enhance the generated state space and obtain good solutions. We define the parallel generation of the state space as an optimization problem, and we propose an ant-based clusteringalgorithm to optimize the parallel generation in terms of balancing the workload and minimizing the communications overhead. Earlier experimental measures performed on cluster of machines have shown very promising results.
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