Swarm intelligence for self-organized clustering
Michael C. Thrun,Alfred Ultsch +1 more
- Vol. 290, pp 103237
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TLDR
The Databionic swarm, called DBS, is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space and can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering and Ward.Abstract:
Algorithms implementing populations of agents which interact with one another and sense their environment may exhibit emergent behavior such as self-organization and swarm intelligence. Here a swarm system, called Databionic swarm (DBS), is introduced which is able to adapt itself to structures of high-dimensional data characterized by distance and/or density-based structures in the data space. By exploiting the interrelations of swarm intelligence, self-organization and emergence, DBS serves as an alternative approach to the optimization of a global objective function in the task of clustering. The swarm omits the usage of a global objective function and is parameter-free because it searches for the Nash equilibrium during its annealing process. To our knowledge, DBS is the first swarm combining these approaches. Its clustering can outperform common clustering methods such as K-means, PAM, single linkage, spectral clustering, model-based clustering, and Ward, if no prior knowledge about the data is available. A central problem in clustering is the correct estimation of the number of clusters. This is addressed by a DBS visualization called topographic map which allows assessing the number of clusters. It is known that all clustering algorithms construct clusters, irrespective of the data set contains clusters or not. In contrast to most other clustering algorithms, the topographic map identifies, that clustering of the data is meaningless if the data contains no (natural) clusters. The performance of DBS is demonstrated on a set of benchmark data, which are constructed to pose difficult clustering problems and in two real-world applications.read more
Citations
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Swarm Intelligence From Natural To Artificial Systems
TL;DR: Swarm intelligence from natural to artificial systems, where people have search hundreds of times for their chosen books, but end up in malicious downloads instead of reading a good book with a cup of coffee in the afternoon.
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Analyzing the fine structure of distributions.
TL;DR: A new visualization tool called the mirrored density plot (MD plot), which is specifically designed to discover interesting structures in continuous features, is proposed, and it is shown that when statistical testing poses a great difficulty, only the MD plots can identify the structure of their PDFs.
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Using Projection-Based Clustering to Find Distance- and Density-Based Clusters in High-Dimensional Data
Michael C. Thrun,Alfred Ultsch +1 more
TL;DR: A comparison showed that PBC is always able to find the correct cluster structure, while the performance of the best of the 32 clustering algorithms varies depending on the dataset.
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