Results 1 to 10 of about 794 (166)

DISCOVERING CONFUSING FREQUENT ITEMSETS

Tạp chí Khoa học Đại học Đà Lạt, 2018
Frequent itemset mining is one of the most important research areas in the field of association rule mining. Exploiting frequent itemsets at different abstraction levels of data will yield valuable knowledge.
Huỳnh Thành Lộc
doaj   +2 more sources

Quick mining in dense data: applying probabilistic support prediction in depth-first order [PDF]

PeerJ Computer Science
Frequent itemset mining (FIM) is a major component in association rule mining, significantly influencing its performance. FIM is a computationally intensive nondeterministic polynomial time (NP)-hard problem.
Muhammad Sadeequllah, Azhar Rauf, Saif Ur Rehman, Noha Alnazzawi   +3 more
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TKFIM: Top-K frequent itemset mining technique based on equivalence classes [PDF]

PeerJ Computer Science, 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.
Saood Iqbal, Abdul Shahid, Muhammad Roman, Zahid Khan, Shaha Al-Otaibi, Lisu Yu   +5 more
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Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Applied Sciences, 2021
When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets.
Yalong Zhang, Wei Yu, Qiuqin Zhu, Xuan Ma, Hisakazu Ogura   +4 more
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Finding Stable Periodic-Frequent Itemsets in Big Columnar Databases

IEEE Access, 2023
Stable periodic-frequent itemset mining is essential in big data analytics with many real-world applications. It involves extracting all itemsets exhibiting stable periodic behaviors in a temporal database.
Hong N. Dao, Penugonda Ravikumar, Palla Likhitha, Uday Kiran Rage, Yutaka Watanobe, Incheon Paik   +5 more
doaj   +1 more source

Efficient mining of intra-periodic frequent sequences

Array, 2022
Frequent Sequence Mining (FSM) is a fundamental task in data mining. Although FSM algorithms extract frequent patterns, they cannot discover patterns that periodically appear in the data.
Edith Belise Kenmogne, Clémentin Tayou Djamegni, Roger Nkambou, Laurent Cabrel Tabueu Fotso, Calvin Tadmon   +4 more
doaj   +1 more source

Association Mining for Super Market Sales using UP Growth and Top-K Algorithm [PDF]

ITM Web of Conferences, 2020
Frequent itemsets(HUIs) mining is an evolving field in data mining, that centers around finding itemsets having a utility that meets a user-specified minimum utility by finding all the itemsets.
Bhope Harshal, Mahajan Yash, Deore Swapnil, Jethani Vimla   +3 more
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An algebraic semigroup method for discovering maximal frequent itemsets

Open Mathematics, 2022
Discovering maximal frequent itemsets is an important issue and key technique in many data mining problems such as association rule mining. In the literature, generating maximal frequent itemsets proves either to be NP-hard or to have O(l34l(m+n))O\left({
Liu Jiang, Li Jing, Ni Feng, Xia Xiang, Li Shunlong, Dong Wenhui   +5 more
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High Scalability Document Clustering Algorithm Based On Top-K Weighted Closed Frequent Itemsets

Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 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.
Gede Aditra Pradnyana, Arif Djunaidy
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Weighted Frequent Itemsets Mining Algorithm Based on Difference Nodeset [PDF]

Jisuanji gongcheng, 2020
To address the low mining efficiency of NFWI,a WN-list based algorithm for weighted frequent itemsets mining,this paper proposes a WDiffNodeset-based weighted frequent itemsets mining algorithm,DiffNFWI.The algorithm extends the data structure of ...
WANG Bin, FANG Xinxiu, WEI Tianyou
doaj   +1 more source