Despite outstanding successes of the state-of-the-art clustering algorithms, many of them still suffer from shortcomings. Mainly, these algorithms do not capture coherency and homogeneity of clusters simultaneously. We show that some of the best performing spectral as well as hierarchical clustering algorithms can lead to incorrect clustering when the data is comprised of clusters with differen...

Frames which can be generated by the action of some operators (e.g. translation, dilation, modulation, ...) on a single element $f$ in a Hilbert space, called coherent frames. In this paper, we introduce a class of continuous frames in a Hilbert space $mathcal{H}$ which is indexed by some locally compact group $G$, equipped with its left Haar measure. These frames are obtained as the orbits of ...

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show low performance for data that inherently suffer from noise, resulting in temporally inconsistent clusters. We propose an evolutionary clusteri...

Clustering algorithms are often limited by an assumption that each data point belongs to a single class, and furthermore that all features of a data point are relevant to class determination. Such assumptions are inappropriate in applications such as gene clustering, where, given expression profile data, genes may exhibit similar behaviors only under some, but not all conditions, and genes may ...

In this paper, we consider non-coherent detection schemes for molecular communication systems in the presence of inter-symbol-interference. particular, study detectors based on memory-bits-based thresholds order to achieve low bit-error-ratio (BER) transmission. The main challenge realizing is obtain channel state information only received signals. We tackle issue by reformulating through inter...

This paper presents an effective parameter-less graph based clustering technique (GCEPD). GCEPD produces highly coherent clusters in terms of various cluster validity measures. The technique finds highly coherent patterns containing genes with high biological relevance. Experiments with real life datasets establish that the method produces clusters that are significantly better than other simil...

Discovering co-expressed genes and coherent expression patterns in gene expression data is an important data analysis task in bioinformatics research and biomedical applications. Although various clustering methods have been proposed, two tough challenges still remain on how to integrate the users’ domain knowledge and how to handle the high connectivity in the data. Recently, we have systemati...

The accurate and automatic segmentation of tissue regions in cervigram images can aid in the identification and classification of precancerous regions. We implement and analyze four GPU (Graphics Processing Unit) based clustering algorithms: K-means, mean shift, deterministic annealing, and spatially coherent deterministic annealing. From our results, we propose a novel parallel algorithm using...