Techniques to explore time-related correlation in large datasets

The next generation of database management and computing systems will be significantly complex with data distributed both in functionality and operation. The complexity arises, at least in part, due to data types involved and types of information request rendered by the database user. Time sequence databases are generated in many practical applications. Detecting similar sequences and subsequences within these databases is an important research area and has generated lot of interest recently. Previous studies in this area have concentrated on calculating similitude between (sub)sequences of equal sizes. The question of unequal sized (sub)sequence comparison to report similitude has been an open problem for some time. The problem is an important and non-trivial one. In this dissertation, we propose a solution to the problem of finding sequences, in a database of unequal sized sequences, that are similar to a given query sequence. A paradigm to search pairs of similar, equal and unequal sized, subsequences within a pair of sequences is also presented. We put forward new approaches for sequence time-scale reduction, feature aggregation and object recognition. To make the search of similar sequences efficient, we propose an indexing technique to index the unequal-sized sequence database. We also introduce a unique indexing technique to index identified subsequences within a reference sequence. This index is subsequently employed to report similar pairs of subsequences, when presented with a query sequence. We present several experimental results and also compare the proposed framework with previous work in this area

Segmentation of Fluorescence Microscopy Cell Images Using Unsupervised Mining

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsupervised mining approaches of clustering can be incorporated in the segmentation of cells. In this study, we have developed and evaluated the performance of multiple unsupervised data mining techniques in cell image segmentation. We adapt four distinctive, yet complementary, methods for unsupervised learning, including those based on k-means clustering, EM, Otsu’s threshold, and GMAC. Validation measures are defined, and the performance of the techniques is evaluated both quantitatively and qualitatively using synthetic and recently published real data. Experimental results demonstrate that k-means, Otsu’s threshold, and GMAC perform similarly, and have more precise segmentation results than EM. We report that EM has higher recall values and lower precision results from under-segmentation due to its Gaussian model assumption. We also demonstrate that these methods need spatial information to segment complex real cell images with a high degree of efficacy, as expected in many medical informatics applications

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Dedicated to my late grandfather Shri. Bihari Lal Dua for his vision and direction. ii Acknowledgements Many people were involved in the completion of this dissertation and my studies at th

Machine learning in healthcare informatics

The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries