4,729 research outputs found
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
Development of Manufacturing Cells Using an Artificial Ant-Based Algorithm with Different Similarity Coefficients
Although there exists several ways of solving the cellular manufacturing problem, including several ant-based algorithms, many of these algorithms focus on obtaining the best possible answer instead of efficiency. An existing artificial-ant based algorithm AntClass, was modified so that it is easier to manipulate. AntClass uses Euclidean vectors to measure the similarity between parts, because similarity is used to group parts together instead of distances, the modified version uses similarity coefficients. The concept of heaping clusters was also introduced to ant algorithms for cellular manufacturing. Instead of using Euclidean vectors to measure the distance to the center of a heap, as in the AntClass algorithm, an average similarity was introduced to measure the similarity between a part and a heap. The algorithm was tested on five common similarity coefficients to determine the similarity coefficient which gives the better quality solution and the most efficient process
Extremal optimization for sensor report pre-processing
We describe the recently introduced extremal optimization algorithm and apply
it to target detection and association problems arising in pre-processing for
multi-target tracking.
Here we consider the problem of pre-processing for multiple target tracking
when the number of sensor reports received is very large and arrives in large
bursts. In this case, it is sometimes necessary to pre-process reports before
sending them to tracking modules in the fusion system. The pre-processing step
associates reports to known tracks (or initializes new tracks for reports on
objects that have not been seen before). It could also be used as a pre-process
step before clustering, e.g., in order to test how many clusters to use.
The pre-processing is done by solving an approximate version of the original
problem. In this approximation, not all pair-wise conflicts are calculated. The
approximation relies on knowing how many such pair-wise conflicts that are
necessary to compute. To determine this, results on phase-transitions occurring
when coloring (or clustering) large random instances of a particular graph
ensemble are used.Comment: 10 page
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