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Data mining and knowledge discovery is an active research work and getting popular by the day because it can be applied in different type of data like web click streams, sensor networks, stock exchange data and time-series data and so on. Data streams are not devoid of research problems. This is attributed to non-stop data arrival in numerous, swift, varying with time, erratic and unrestricted data field. It is highly important to find the regular prototype in single pass data stream or minor number of passes when making use of limited space of memory. In this survey the review on the final progress in the study of regular model mining in data streams. Mining algorithms are talked about at length and further research directions have been suggested.
V.Sidda Reddy. 1970. \u201cBenchmark Algorithms and Models of Frequent Itemset Mining over Data Streams: Contemporary Affirmation of State of Art\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 13 (GJCST Volume 13 Issue C5): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Total Score: 113
Country: India
Subject: Global Journal of Computer Science and Technology - C: Software & Data Engineering
Authors: V.Sidda Reddy, Dr.T.V.Rao, Dr.A.Govardhan (PhD/Dr. count: 2)
View Count (all-time): 288
Total Views (Real + Logic): 25387
Total Downloads (simulated): 10892
Publish Date: 1970 01, Thu
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Data mining and knowledge discovery is an active research work and getting popular by the day because it can be applied in different type of data like web click streams, sensor networks, stock exchange data and time-series data and so on. Data streams are not devoid of research problems. This is attributed to non-stop data arrival in numerous, swift, varying with time, erratic and unrestricted data field. It is highly important to find the regular prototype in single pass data stream or minor number of passes when making use of limited space of memory. In this survey the review on the final progress in the study of regular model mining in data streams. Mining algorithms are talked about at length and further research directions have been suggested.
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