Zobrazeno 1 - 10
of 1 375
pro vyhledávání: '"Frequent Itemsets"'
Publikováno v:
Tạp chí Khoa học, Vol 53, Iss 2A, Pp 56-72 (2024)
High utility itemsets (HUIs) mining is the finding of itemsets that satisfy a user-defined minimum utility threshold. Many successful studies in this field have been carried out, however they are all reliant on Tidset techniques, which records the
Externí odkaz:
https://doaj.org/article/178917586ac840dca71e8a9a75a3311f
Publikováno v:
IEEE Access, Vol 12, Pp 39330-39350 (2024)
Frequent itemset mining (FIM) is a highly resource-demanding data-mining task fundamental to numerous data-mining applications. Support calculation is a frequently performed computation-intensive operation of FIM algorithms, whereas storing transacti
Externí odkaz:
https://doaj.org/article/1ef75d21804c41f48f78bf7d313db05e
Publikováno v:
IEEE Access, Vol 12, Pp 6281-6297 (2024)
Data analytics is an integral part of strategic decision making in various fields but not limited to business, education and healthcare systems. Existing research works focus on the discovery of itemsets with rare antecedents and consequent or freque
Externí odkaz:
https://doaj.org/article/3886b22c43a34cdc83fd7f878fcdca78
Autor:
Gede Aditra Pradnyana, Arif Djunaidy
Publikováno v:
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), Vol 5, Iss 2, Pp 359-368 (2021)
Documents clustering based on frequent itemsets can be regarded a new method of documents clustering which is aimed to overcome curse of dimensionality of items produced by documents being clustered. The Maximum Capturing (MC) technique is an algorit
Externí odkaz:
https://doaj.org/article/929353eee74848f584deecad495312a7
Autor:
Weixing WANG, Zhaowei LIU
Publikováno v:
Taiyuan Ligong Daxue xuebao, Vol 52, Iss 2, Pp 282-291 (2021)
A method was preposed for mining conditional preferences and learning CP-nets on the data stream based on the inverted matrix structure. The transaction layout using the inverted matrix reduces the number of times the database is scanned, and through
Externí odkaz:
https://doaj.org/article/c457f0ab06ef4d2fb01510e5524b4c1e
Publikováno v:
Vietnam Journal of Computer Science, Vol 7, Iss 4, Pp 355-372 (2020)
The analysis of numerical data, whether structured, semi-structured, or raw, is of paramount importance in many sectors of economic, scientific, or simply social activity. The process of extraction of association rules is based on the lexical quality
Externí odkaz:
https://doaj.org/article/d70483ca74374d57bd9fb32cdcd25be3
Autor:
Sikha Bagui, Patrick Stanley
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-20 (2020)
Abstract This paper presents a study of mining frequent itemsets from streaming data in the presence of concept drift. Streaming data, being volatile in nature, is particularly challenging to mine. An approach using genetic algorithms is presented, a
Externí odkaz:
https://doaj.org/article/2610ac8195644b78a3a55f02b0c950ea
Publikováno v:
Journal of Big Data, Vol 7, Iss 1, Pp 1-14 (2020)
Abstract In this paper, a novel method for binary image comparison is presented. We suppose that the image is a set of transactions and items. The proposed method applies along rows and columns of an image; this image is represented by all frequent i
Externí odkaz:
https://doaj.org/article/df0f507082734bbcba8c58bed129830d
Publikováno v:
PeerJ Computer Science, Vol 7, p e385 (2021)
Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenge
Externí odkaz:
https://doaj.org/article/dcf8868a32f74951b225efa4a5348f76
Publikováno v:
Mathematics, Vol 11, Iss 2, p 401 (2023)
A challenge in association rules’ mining is effectively reducing the time and space complexity in association rules mining with predefined minimum support and confidence thresholds from huge transaction databases. In this paper, we propose an effic
Externí odkaz:
https://doaj.org/article/6768216719954cd09644e74b627d3a3c