| Type of Material: | Thesis |
| Title: | Associative classification techniques for designing efficient algorithms for data stream mining |
| Researcher: | Prasanna Lakshmi T. |
| Guide: | Ramesh Kumar Reddy C. |
| Department: | Faculty of Computer Science and Engineering |
| Publisher: | Jawaharlal Nehru Technological University, Hyderabad |
| Place: | Hyderabad |
| Year: | 2015 |
| 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; 2015 |
| Fulltext: | Shodhganga |
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| 035 | __ | |a(IN-AhILN)th_454183 |
| 040 | __ | |aJNTU_500028|dIN-AhILN |
| 041 | __ | |aeng |
| 100 | __ | |aPrasanna Lakshmi T.|eResearcher |
| 110 | __ | |aFaculty of Computer Science and Engineering|bJawaharlal Nehru Technological University, Hyderabad|dHyderabad|ein|0U-0017 |
| 245 | __ | |aAssociative classification techniques for designing efficient algorithms for data stream mining |
| 260 | __ | |aHyderabad|bJawaharlal Nehru Technological University, Hyderabad|c2015 |
| 300 | __ | |dDVD |
| 502 | __ | |bPhD|cFaculty of Computer Science and Engineering, Jawaharlal Nehru Technological University, Hyderabad, Hyderabad|d2015 |
| 518 | __ | |oDate of Award|dAugust 2015 |
| 518 | __ | |oDate of Registration|d2009-01-01 |
| 520 | __ | |aIn recent years, advancement in technologies has made it possible fornewlinemost of the present day organizations to store and record large streams of data. Such datasets which continuously and rapidly grow over time are referred to as data streams. Progress of technologies has resulted in the possibility of monitoring these data streams in real time. Mining ofnewlinesuch data streams is a unique opportunity and even a challenging task. Data Stream Mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past.newlineTraditional data mining has become equitably a well-established field now, but it focusses on high accuracy with limited data. Continuously flowing and rapidly growing data streams cannot be directly handled by traditional machine learning techniques including frequent item set mining, classification, associative classification and clustering.newlineThe aim of |
| 650 | __ | |aComputer Science and Information Technology|2UGC |
| 650 | __ | |aEngineering and Technology|2AIU |
| 700 | __ | |aRamesh Kumar Reddy C.|eGuide |
| 856 | __ | |uhttp://shodhganga.inflibnet.ac.in/handle/10603/287853|yShodhganga |
| 905 | __ | |afromsg |
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