Novel Algorithms and Applications for Data Mining and Machine Learning
Digital Document
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Handle
http://hdl.handle.net/11134/20002:860655126
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Persons |
Persons
Creator (cre): Pathak, Sudipta
Major Advisor (mja): Rajasekaran, Sanguthevar
Associate Advisor (asa): Ammar, Reda
Associate Advisor (asa): Mandoiu, Ion
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Title |
Title
Title
Novel Algorithms and Applications for Data Mining and Machine Learning
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Origin Information |
Origin Information
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Parent Item
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Resource Type
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Digital Origin |
Digital Origin
born digital
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Description |
Description
In this dissertation we propose novel approaches for data mining and machine learn- ing for some of the fundamental and advanced problems in the areas of data analysis and bioinformatics. A number of problems such as Drug-Target Interaction (DTI) pre- diction, Singular Value Decomposition (SVD), Time series Analysis (TSA) and motif search has been studied in this thesis. We have developed algorithms that outperformed the state of art in all of the above mentioned areas. We proposed algorithms for DTI prediction that outperformed all prior algorithms over benchmark datasets under mul- tiple scenarios. Our proposed approaches for Jacobi based SVD, both sequential and parallel, improves the computation time and work compared to the state of art. We also proposed ensemble based TSA models that improves classification accuracy sta- tistically significantly compared to all prior algorithm on benchmark dataset. We also introduced novel motif search algorithms for DNA and protein motifs.
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Genre
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Organizations |
Organizations
Degree granting institution (dgg): University of Connecticut
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Use and Reproduction |
Use and Reproduction
These Materials are provided for educational and research purposes only.
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Note |
Note
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Degree Name |
Degree Name
Doctor of Philosophy
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Degree Level |
Degree Level
Doctoral
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Degree Discipline |
Degree Discipline
Computer Science and Engineering
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Local Identifier |
Local Identifier
OC_d_2594
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