Current nonnegative matrix factorization (NMF) deals with X = FG type. We provide a systematic analysis and extensions of NMF to the symmetric W = HH , and the weighted W = HSH . We show that (1) W = HH is equivalent to Kernel K-means clustering and the Laplacian-based spectral clustering. (2) X = FG is equivalent to simultaneous clustering of rows and columns of a bipartite graph. Algorithms a...

We present a clustering approach for documents returned by a PubMed search, which enable the organisation of evidence underpinning clinical recommendations for Evidence Based Medicine. Our approach uses a combination of document similarity metrics, which are fed to an agglomerative hierarchical clusterer. These metrics quantify the similarity of published abstracts from syntactic, semantic, and...

Clustering techniques have been widely used in automatic videosummarization applications to group shots with comparable content. We enhance the popular k-means clustering algorithm to integrate user-supplied domain-knowledge into the cluster generation step. This provides a convenient way to exclude scenes from the summary which are a-priori known to be irrelevant. Furthermore, we added an addi...

The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the Baire metric with (i) agglomerative hierarchical clustering, in terms of algo...

While students’ skill set profiles can be estimated with formal cognitive diagnosis models [8], their computational complexity makes simpler proxy skill estimates attractive [1, 4, 6]. These estimates can be clustered to generate groups of similar students. Often hierarchical agglomerative clustering or k-means clustering is utilized, requiring, for K skills, the specification of 2K clusters. T...

Searching and mining biomedical literature databases are common ways of generating scientific hypotheses by biomedical researchers. Clustering can assist researchers to form hypotheses by seeking valuable information from grouped documents effectively. Although a large number of clustering algorithms are available, this paper attempts to answer the question as to which algorithm is best suited ...

We propose a new algorithm for consensus clustering, FCAConsensus, based on Formal Concept Analysis. As the input, the algorithm takes T partitions of a certain set of objects obtained by k-means algorithm after T runs from different initialisations. The resulting consensus partition is extracted from an antichain of the concept lattice built on a formal context objects× classes, where the clas...

In this paper we present a comparison among some nonhierarchical and hierarchical clustering algorithms including SOM (Self-Organization Map) neural network and Fuzzy c-means methods. Data were simulated considering correlated and uncorrelated variables, nonoverlapping and overlapping clusters with and without outliers. A total of 2530 data sets were simulated. The results showed that Fuzzy c-m...

We present a new clustering algorithm that is based on searching for natural gaps in the components of the lowest energy eigenvectors of the Laplacian of a graph. In comparing the performance of the proposed method with a set of other popular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the context of the Lancichinetti-Fortunato-Radicchi (LFR) Benchmark for undirected weigh...

This paper presents a strategy for shape-based image retrieval in which moment invariants form a feature vector to describe the shape of an object. Fuzzy k-means clustering is used to group similar images in an image collection into k-clusters whereas neural network is used to facilitate efficient retrieval of similar images against a given user-provided query image. Retrieval results and perfo...