A Survey on Mining Frequent Itemsets over Data Streams

Shailvi Maurya, Sneha Ambhore, Sneha Parit
<span title="2017-12-15">2017</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
Mining frequent itemsets over data stream has been challenging task. The incoming data from various sources like ecommerce website, click streams, text, audio, weather forecasting etc. are massive unbounded and high speed that it is impractical to store all, process and scan complete data at the same time to extract information. While processing memory and time are the main parameters must be minimum consumed. Thus the paper provides different algorithms for mining over static and dynamic data also known as data stream.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2017916030">doi:10.5120/ijca2017916030</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ojkfobsynnedhhp7d7ayxabo7a">fatcat:ojkfobsynnedhhp7d7ayxabo7a</a> </span>
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