Ant-based clustering and sorting is a nature-inspired heuristic for general clustering tasks. It has been applied variously, from problems arising in commerce, to circuit design, to text-mining, all with some promise. However, although early results were broadly encouraging, there has been very limited analytical evaluation of the algorithm. Toward this end, we first propose a scheme that enables unbiased interpretation of the clustering solutions obtained, and then use this to conduct a full evaluation of the algorithm. Our analysis uses three sets each of real and artificial data, and four distinct analytical measures. These results are compared with those obtained using established clustering techniques and we find evidence that ant-based clustering is a robust and viable alternative.

Ant-based clustering and sorting is a nature-inspired heuristic for general clustering tasks. It has been applied variously, from problems arising in commerce, to circuit design, to text-mining, all with some promise. However, although early results were broadly encouraging, there has been very limited analytical evaluation of antbased clustering. Toward this end, we first propose a scheme that enables unbiased interpretation of the clustering solutions obtained, and then use this to conduct a full evaluation of the algorithm. Our analysis uses three sets each of real and artificial data, and four distinct analytical measures. These results are compared with those obtained using established clustering techniques and we find evidence that ant-based clustering is a robust and viable alternative.

Abstract Ant-based clustering and sorting is a nature-inspired heuristic first introduced as a model for explaining two types of emergent behavior observed in real ant colonies. More recently, it has been applied in a data-mining context to perform both clustering and topographic mapping. Early work demonstrated some promising characteristics of the heuristic but did not extend to a rigorous investigation of its capabilities. We describe an improved version, called ATTA, incorporating adaptive, heterogeneous ants, a time-dependent transporting activity, and a method (for clustering applications) that transforms the spatial embedding produced by the algorithm into an explicit partitioning. ATTA is then subjected to the most rigorous experimental evaluation of an ant-based clustering and sorting algorithm undertaken to date: we compare its performance with standard techniques for clustering and topographic mapping using a set of analytical evaluation functions and a range of synthetic and real data collections. Our results demonstrate the ability of ant-based clustering and sorting to automatically identify the number of clusters inherent in a data collection, and to produce high quality solutions; indeed, we show that it is particularly robust for clusters of differing sizes and for overlapping clusters. The results obtained for topographic mapping are, however, disappointing. We provide evidence that the solutions generated by the ant algorithm are barely topology-preserving, and we explain in detail why results have—in spite of this—been misinterpreted (much more positively) in previous research.

this paper we show how the combination of the ant-based approach with fuzzy rules leads to an algorithm which is conceptually simpler, more e#cient and more robust than previous approaches

Abstract — The objective of this paper is to evaluate and detect muscular tiredness in natural activities, in particular, to select the most comfortable car seat. This work consists to identifying and to classify the EMG signal of the techniques from data mining and from the statistical techniques. We thus tried hybridization between some to lead to a better separation between the classes. The methods of clustering will be applied to some signals resulting from the experiments of discomfort of long duration on seats of different vehicles. These methods consist in separating segments EMG in two classes corresponding to the frequential variation from EMG signal. Copyright c ○ 2005 Yang’s Scientific

Algorithms for clustering web search results have to be efficient and robust. Furthermore they must be able to cluster a dataset without using any kind of a priori information, such as the required number of clusters. Clustering algorithms inspired by the behaviour of real ants generally meet these requirements. In this paper we propose a novel approach to ant based clustering, based on fuzzy logic. We show that it improves existing approaches and illustrate how our algorithm can be applied to the problem of web search results clustering. 1

Person name queries often bring up web pages that correspond to individuals sharing the same name. The Web People Search (WePS) task consists of organizing search results for ambiguous person name queries into meaningful clusters, with each cluster referring to one individual. This paper presents a fuzzy ant based clustering approach for this multi-document person name disambiguation problem. The main advantage of fuzzy ant based clustering, a technique inspired by the behavior of ants clustering dead nestmates into piles, is that no specification of the number of output clusters is required. This makes the algorithm very well suited for the Web Person Disambiguation task, where we do not know in advance how many individuals each person name refers to. We compare our results with state-of-the-art partitional and hierarchical clustering approaches (k-means and Agnes) and demonstrate favorable results. This is particularly interesting as the latter involve manual setting of a similarity threshold, or estimating the number of clusters in advance, while the fuzzy ant based clustering algorithm does not.