Results 91 to 100 of about 2,506 (215)

TRICE: Mining Frequent Itemsets by Iterative TRimmed Transaction LattICE in Sparse Big Data

IEEE Access, 2019
Sparseness is often witnessed in big data emanating from a variety of sources, including IoT, pervasive computing, and behavioral data. Frequent itemset mining is the first and foremost step of association rule mining, which is a distinguished ...
Muhammad Yasir, Muhammad Asif Habib, Muhammad Ashraf, Shahzad Sarwar, Muhammad Umar Chaudhry, Hamayoun Shahwani, Mudassar Ahmad, Ch. Muhammad Nadeem Faisal   +7 more
doaj   +1 more source

Frequent Pattern mining with closeness Considerations: Current State of the art

, 2011
Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on
Dr. J.L. Rana, Anurag Choubey
core  

Incremental Closed Frequent Itemsets Mining-Based Approach Using Maximal Candidates

IEEE Access
Incremental frequent itemset mining aims to efficiently update frequent itemsets without recalculating them from scratch, making it suitable for streaming data and real-time analytics.
Mohammed A. Al-Zeiadi, Basheer M. Al-Maqaleh   +1 more
doaj   +1 more source

Similarity processing in multi-observation data [PDF]

, 2012
Many real-world application domains such as sensor-monitoring systems for environmental research or medical diagnostic systems are dealing with data that is represented by multiple observations.
Bernecker, Thomas, Thomas Bernecker
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Infrequent Weighted Itemset Mining Using Frequent Pattern Growth [PDF]

, 2014
Frequent weighted itemsets represent correlations frequently holding in data in which items may weight differently. However, in some contexts, e.g., when the need is to minimize a certain cost function, discovering rare data correlations is more ...
CAGLIERO, LUCA, GARZA, PAOLO
core   +1 more source

Efficient Top-k Frequent Itemset Mining on Massive Data

Data Science and Engineering
Top-k frequent itemset mining (top-k FIM) plays an important role in many practical applications. It reports the k itemsets with the highest supports.
Xiaolong Wan, Xixian Han
doaj   +1 more source

Probabilistic Support Prediction: Fast Frequent Itemset Mining in Dense Data

IEEE Access
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 transactional
Muhammad Sadeequllah, Azhar Rauf, Saif Ur Rehman, Noha Alnazzawi   +3 more
doaj   +1 more source

A NOVEL ALGORITHM FOR ASSOCIATION RULE MINING FROM DATA WITH INCOMPLETE AND MISSING VALUES [PDF]

ICTACT Journal on Soft Computing, 2011
Missing values and incomplete data are a natural phenomenon in real datasets. If the association rules mine incomplete disregard of missing values, mistaken rules are derived.
K. Rameshkumar
doaj  

TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns

IEEE Access, 2018
High-utility itemset mining (HUIM) has been gaining popularity in the field of data mining. Frequent itemset mining used to be the main tool to reveal high-frequency patterns but failed to consider the concept of profit.
Jimmy Ming-Tai Wu, Jerry Chun-Wei Lin, Matin Pirouz, Philippe Fournier-Viger   +3 more
doaj   +1 more source

Privacy-preserving Frequent Itemset Mining for Sparse and Dense Data [PDF]

, 2015
. Frequent itemset mining is a task that can in turn be used for other purposes such as associative rule mining. One problem is that the data may be sensitive, and its owner may refuse to give it for analysis in plaintext.
Alisa Pankova, Peeter Laud
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