Mining Top-K Click Stream Sequences Patterns release_uhtd7pcrejcjbckpzmis3jqmam

by MEHDI Haj Ali, Qun-Xiong Zhu, Yan-Lin He

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abstracts [{'sha1': '3c2bed429c26cdb983ccedc05205f07c8b233049', 'content': '<em>Sequential pattern mining, it\xa0 is not just important in data mining field , but \xa0it is the basis of many applications .However, running applications cost time and memory, especially when dealing with dense of the dataset. Setting the proper minimum support threshold is one of the factors that consume more memory and time. However ,\xa0 it is difficult for users to get the appropriate patterns, it may present too many sequential patterns\xa0 and makes it difficult for users to comprehend the results. The problem becomes worse and worse when dealing with long click stream sequences or huge dataset. As a solution, we developed an efficient algorithm, called TopK (Top-K click stream sequence pattern mining), which employs the output as top-k patterns , K is the most important and relevant frequencies (with a high support) . However ,our algorithm based on pseudo-projection to avoid consuming more time and memory, and uses several efficient search space pruning methods together with BI-Directional Extension. Our extensive study and experiments on real click stream datasets show TopK significantly outperforms the previous algorithms.</em>', 'mimetype': 'application/xml+jats', 'lang': None}]
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pages 655
publisher Institute of Advanced Engineering and Science
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release_date 2016-12-18
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release_year 2016
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title Mining Top-K Click Stream Sequences Patterns
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crossref.license [{'URL': 'http://creativecommons.org/licenses/by-nc-nd/4.0', 'content-version': 'unspecified', 'delay-in-days': 0, 'start': '2016-12-18T00:00:00Z'}]
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