Benchmark Algorithms and Models of Frequent Itemset Mining over Data Streams: Contemporary Affirmation of State of Art

α
V.Sidda Reddy
V.Sidda Reddy
σ
Dr.T.V.Rao
Dr.T.V.Rao
ρ
Dr.A.Govardhan
Dr.A.Govardhan
α Jawaharlal Nehru Technological University, Hyderabad

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Benchmark Algorithms and Models of Frequent Itemset Mining over Data Streams: Contemporary Affirmation of State of Art

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Abstract

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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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

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): .

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Crossref Journal DOI 10.17406/gjcst

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e-ISSN 0975-4172

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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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Benchmark Algorithms and Models of Frequent Itemset Mining over Data Streams: Contemporary Affirmation of State of Art

V.Sidda Reddy
V.Sidda Reddy Jawaharlal Nehru Technological University, Hyderabad
Dr.T.V.Rao
Dr.T.V.Rao
Dr.A.Govardhan
Dr.A.Govardhan

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