2,230 research outputs found

    Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures

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    This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures

    LinkCluE: A MATLAB Package for Link-Based Cluster Ensembles

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    Cluster ensembles have emerged as a powerful meta-learning paradigm that provides improved accuracy and robustness by aggregating several input data clusterings. In particular, link-based similarity methods have recently been introduced with superior performance to the conventional co-association approach. This paper presents a MATLAB package, LinkCluE, that implements the link-based cluster ensemble framework. A variety of functional methods for evaluating clustering results, based on both internal and external criteria, are also provided. Additionally, the underlying algorithms together with the sample uses of the package with interesting real and synthetic datasets are demonstrated herein.

    Meta Clustering

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    Clustering is ill-defined. Unlike supervised learning where labels lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for one optimal clustering based on a pre-specified clustering criterion. Once that clustering has been determined, no further clusterings are examined. Our approach differs in that we search for many alternate reasonable clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Any reasonable partitioning of the data is potentially useful for some purpose, regardless of whether or not it is optimal according to a specific clustering criterion. Our approach first finds a variety of reasonable clusterings. It then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. In this paper, we present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems, and then apply meta clustering to two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings
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