Results 91 to 100 of about 1,488,747 (207)

TIFIM: Tree based Incremental Frequent Itemset Mining over Streaming Data

, 2013
Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these constraints is inversely proportional to the streaming speed of the data.
Dr A. Govardhan, V.sidda Reddy, Dr T.V. Rao   +2 more
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

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 ITEMSETS MINING FOR BIG DATA

, 2019
Frequent Itemsets Mining (FIM) is a fundamental mining model and plays an important role in Data Mining. It has a vast range of application fields and can be employed as a key calculation phase in many other mining models such as Association Rules, Correlations, Classifications, etc. Generally speaking, FIM counts the frequencies of co-occurrence items,
openaire   +2 more sources

An Efficient Algorithm for Frequent Pattern Mining for Real-Time Business Intelligence Analytics in Dense Datasets [PDF]

Finding frequent patterns from databases has been the most time consuming process in data mining tasks, like association rule mining. Frequent pattern mining in real-time is of increasing thrust in many business applications such as e-commerce ...
Dass, Rajanish
core  

Survey On Moving Towards Frequent Pattern Growth for Infrequent Weighted Itemset Mining [PDF]

, 2014
In data mining and knowledge discovery technique domain, frequent pattern mining plays an important role but it does not consider different weight value of the items. Association Rule Mining is to find the correlation between data.
, Miss. Rituja M. Zagade, Prof. Megha V. Borole
core   +1 more source

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

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

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
core   +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