| Type of Material: | Thesis |
| Title: | Fractal based metric and techniques for clustering data streams |
| Researcher: | Anuradha Yarlagadda |
| Guide: | Krishna Prasad M H M. | Murthy J V R |
| Department: | Faculty of Computer Science and Engineering |
| Publisher: | Jawaharlal Nehru Technological University, Hyderabad |
| Place: | Hyderabad |
| Year: | 2016 |
| Language: | English |
| Subject: | Computer Science and Information Technology | Engineering and Technology |
| Dissertation/Thesis Note: | PhD; Faculty of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Hyderabad; 2016 |
| Fulltext: | Shodhganga |
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| 035 | __ | |a(IN-AhILN)th_454184 |
| 040 | __ | |aJNTU_500028|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aAnuradha Yarlagadda|eResearcher |
| 110 | __ | |aFaculty of Computer Science and Engineering|bJawaharlal Nehru Technological University, Hyderabad|dHyderabad|ein|0U-0017 |
| 245 | __ | |aFractal based metric and techniques for clustering data streams |
| 260 | __ | |aHyderabad|bJawaharlal Nehru Technological University, Hyderabad|c2016 |
| 300 | __ | |dDVD |
| 502 | __ | |cFaculty of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Hyderabad|d2016|bPhD |
| 518 | __ | |dMarch 2016|oDate of Award |
| 518 | __ | |oDate of Registration|d2007-01-01 |
| 520 | __ | |aIn recent years, there is a tremendous collection of data coming from different locations and the interest in analyzing and extracting useful patterns from large volume of online data has grown. Suchnewlinedata, known as data streams, are generated at high rates by different environments such as satellite systems and network monitoring applications etc.newlineIn contrast to the traditional data, data streams arrive online and they are massive. They change continuously with respect to time and should be processed on fly. Hence analyzing and understanding data streams is not an easy task, due to the limitations of computationalnewlineresources such as memory constraint to store all data and processing time to compute (sometimes infinite) incoming data. As the data evolve rapidly over time, understanding the model of its behavior becomes much more complex.newlineFor this reason, many conventional algorithms, even proved to be effective, cannot be applicable to data streams. Moreover, most of the traditional da |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 700 | __ | |aKrishna Prasad M H M.|eGuide |
| 700 | __ | |aMurthy J V R|eCo-Guide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/287854|yShodhganga |
| 905 | __ | |afromsg |
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