Results 71 to 80 of about 9,424 (197)
Class Association Rule Pada Metode Associative Classification
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2011 Frequent patterns (itemsets) discovery is an important problem in associative classification rule mining. Differents approaches have been proposed such as the Apriori-like, Frequent Pattern (FP)-growth, and Transaction Data Location (Tid)-list ...
Eka Karyawati, Edi Winarko
doaj +1 more source
DiffNodesets: An Efficient Structure for Fast Mining Frequent Itemsets
, 2015 Mining frequent itemsets is an essential problem in data mining and plays an important role in many data mining applications. In recent years, some itemset representations based on node sets have been proposed, which have shown to be very efficient for ...
Deng, Zhi-Hong
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IET Communications, Volume 19, Issue 1, January/December 2025.This paper proposed an Automated Quality Assessment of SRS (AQA‐SRS) framework by integrating four popular methods which are; NLP, K‐means, MAS, and CBR to assess the quality of SRS documents. The NLP utilize for feature extraction, K‐means for features clustering, MAS for interactive assessment and feature selection decision, and CBR for managing the ...
Mohammed Ahmed Jubair +6 more
wiley +1 more source
Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees [PDF]
, 2013 The tasks of extracting (top-$K$) Frequent Itemsets (FI's) and Association Rules (AR's) are fundamental primitives in data mining and database applications. Exact algorithms for these problems exist and are widely used, but their running time is hindered
Riondato, Matteo, Upfal, Eli
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An AI knowledge‐based system for police assistance in crime investigation
Expert Systems, Volume 42, Issue 1, January 2025.Abstract The fight against crime is often an arduous task overall when huge amounts of data have to be inspected, as is currently the case when it comes for example in the detection of criminal activity on the dark web. This work presents and describes an artificial intelligence (AI) based system that combines various tools to assist police or law ...
Carlos Fernandez‐Basso +4 more
wiley +1 more source
A Fast Approach for Up-Scaling Frequent Itemsets
IEEE Access, 2020 With the rapid growth of data scale and diversification of demand, people have an urgent desire to extract useful frequent itemset from datasets of different scales. It is no doubt that the traditional method can solve the problem.
Runzi Chen, Shuliang Zhao, Mengmeng Liu
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Advances in Civil Engineering, Volume 2025, Issue 1, 2025.The coal industry has always been a typically high‐risk industry with frequent accidents and extremely adverse impacts. Cases of accidents in coal mine ventilation systems serve as a concentrated demonstration of accident hazards and hold significant value for identifying key risk factors that may induce disasters in coal mine ventilation systems. This
Mingjia Jing +4 more
wiley +1 more source
Most Frequent Itemset Optimization
, 2019 In this paper we are dealing with the frequent itemset mining. We concentrate on the special case that we only want to identify the most frequent itemset of length N.
Nüßlein, Jonas
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Unveiling Success Drivers in Gaming: A Machine Learning Study Across Steam, Twitch, and Metacritic
International Journal of Computer Games Technology, Volume 2025, Issue 1, 2025.This study employs machine learning to assess the relative impact of major platforms—Steam, Twitch, and Metacritic—on video game revenue. Through an integrated analysis of three comprehensive datasets comprising commercially successful titles on Steam, key predictors of financial performance were identified.
Jiesi Ma, Michael J. Katchabaw
wiley +1 more source
A Fast Minimal Infrequent Itemset Mining Algorithm [PDF]
, 2014 A novel fast algorithm for finding quasi identifiers in large datasets is presented. Performance measurements on a broad range of datasets demonstrate substantial reductions in run-time relative to the state of the art and the scalability of the ...
Demchuk, Kostyantyn, Leith, Douglas J.
core +1 more source