Results 41 to 50 of about 846,186 (212)

Frequent Itemset Generation using Double Hashing Technique [PDF]

, 2012
In data mining, frequent itemsets plays an important role which is used to identify the correlations among the fields of database.In this paper, we propose a new association rule mining algorithm called Double Hashing Based Frequent Itemsets, (DHBFI) in ...
Krishnamurthy, M., Jayalakshmi, N., Vidhya, V., Kannan, A.   +3 more
core   +1 more source

EFFICIENT FREQUENT ITEMSET DISCOVERY THROUGH HIERARCHICAL HUFFMAN ENCODING [PDF]

ICTACT Journal on Soft Computing
Frequent itemsets mining holds a crucial position in the field of data mining; however, traditional algorithms like Apriori and FP-Growth often encounter efficiency and memory consumption issues when handling large-scale datasets, which not only makes ...
Dai Xin, Hao Xue
doaj   +1 more source

Closed frequent itemset mining with arbitrary side constraints [PDF]

, 2018
Frequent itemset mining (FIM) is a method for finding regularities in transaction databases. It has several application areas, such as market basket analysis, genome analysis, and drug design. Finding frequent itemsets allows further analysis to focus on
Nightingale, Peter William, Ozgur Akgun, Kocak, Gokberk, Peter Nightingale, Gokberk Kocak, Akgun, Ozgur, Miguel, Ian James, Ian Miguel   +7 more
core   +6 more sources

Frequent Itemset Mining using QUBO [PDF]

, 2022
In this paper we propose a R-step approximation to solve frequent itemset mining on quantum hardware like quantum annealing or QAOA. The idea is to search for the set of items where the minimal 2-item frequency is maximal.
Nüßlein, Jonas
core   +1 more source

A survey of itemset mining

, 2017
Itemset mining is an important subfield of data mining, which consists of discovering interesting and useful patterns in transaction databases. The traditional task of frequent itemset mining is to discover groups of items (itemsets) that appear ...
Jerry Chun‐Wei Lin, Vo, Bay, Bay Vo, Tin Truong Chi, Zhang, Ji, Philippe Fournier‐Viger, Hoai Bac Le, Chi, Tin Truong, Le, Hoai Bac, Lin, Jerry Chun-Wei, Ji Zhang, Fournier-Viger, Philippe   +11 more
core   +1 more source

A novel association rule mining approach using TID intermediate itemset. [PDF]

PLoS ONE, 2018
Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern.
Iyad Aqra, Tutut Herawan, Norjihan Abdul Ghani, Adnan Akhunzada, Akhtar Ali, Ramdan Bin Razali, Manzoor Ilahi, Kim-Kwang Raymond Choo   +7 more
doaj   +1 more source

Association Rule Mining for Accidental Dwelling Fire Incidence Data Analysis in Greater Manchester, UK

Fire and Materials, EarlyView.
ABSTRACT Association rule mining was used to identify patterns in accidental dwelling fire incidents attended by Greater Manchester Fire and Rescue Service over the period 2013/14 to 2023/24. The association rule mining process identified relationships between cooking fire incidence, distraction, living alone, deprivation, and fire injury.
M. Taylor, R. Lyon, D. Doolan, E. Dean, J. Fielding, M. Thomas   +5 more
wiley   +1 more source

Sistem Rekomendasi Buku Perpustakaan Menggunakan Algoritma Frequent Pattern Growth

Techno.Com, 2022
Perpustakaan memiliki pelayanan utama memfasilitasi peminjaman buku, untuk memudahkan anggota perpustakaan menemukan buku yang tepat, perpustakaan dapat dilengkapi dengan sistem pencarian buku.
Endang Retnoningsih, Tia Monisya Afriyanti   +1 more
doaj   +1 more source

Efficient heuristics for the Steiner forest problem

International Transactions in Operational Research, EarlyView.
Abstract Let G=(V,E)$G = (V, E)$ be a connected undirected graph, V$V$ a set of nodes, E$E$ a set of edges, |V|=n$|V| = n$, and |E|=m$|E| = m$. Given a non‐negative weight function w:E→R+$w: E \rightarrow \mathbb {R}^+$ associated with its edges, a set τ={Ti⊆V|i=1,…,p}$\tau = \lbrace T_i \subseteq V | i = 1, \ldots, p\rbrace$ of terminal sets Ti$T_i ...
Murilo Stockinger, Isabel Rosseti, Simone Martins, Luidi Simonetti, Alexandre Plastino   +4 more
wiley   +1 more source

A Robust Technique for Closed Frequent and High Utility Itemsets Mining: Closed-FHUIM

IEEE Access
Frequent itemset mining (FIM) and high utility itemset mining (HUIM) are popular data mining techniques used in various real-world applications such as retail-market, bio-medicine, and click-stream analysis.
Muhammad Waheed Ashraf, M. Asif Naeem, Heejeong Jasmine Lee   +2 more
doaj   +1 more source