Results 71 to 80 of about 739 (208)
AN ADABOOST OPTIMIZED CCFIS BASED CLASSIFICATION MODEL FOR BREAST CANCER DETECTION [PDF]
Journal of Engineering Science and Technology, 2017 Classification is a Data Mining technique used for building a prototype of the data behaviour, using which an unseen data can be classified into one of the defined classes.
CHANDRASEKAR RAVI, NEELU KHARE
doaj
Data‐Driven Materials Research and Development for Functional Coatings
Advanced Science, Volume 11, Issue 42, November 13, 2024.Functional coatings play a vital role in various industries for their protective and functional properties. However, its design often involves time‐consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data‐driven approaches are gaining traction in materials science. This review provides an overview
Kai Xu +8 more
wiley +1 more source
Mining frequent closed itemsets from a landmark window over online data streams
, 2009 The frequent closed itemsets determine exactly the complete set of frequent itemsets and are usually much smaller than the later. However, mining frequent closed itemsets from a landmark window over data streams is a challenging problem.
Liu, Xuejun, Hu, Ping, Guan, Jihong
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IET Generation, Transmission &Distribution, Volume 18, Issue 13, Page 2313-2324, July 2024.This paper proposes an electric vehicle (EV) load diagnosis algorithm considering data privacy. The validity of the algorithm in this paper is verified by using the real collected EV load data. Abstract Accurate forecasting of electric vehicle (EV) load is essential for grid stability and energy management. EV load forecasting is influenced by multiple
Ruien Bian +3 more
wiley +1 more source
Mining Closed Itemsets for Coherent Rules: An Inference Analysis Approach
, 2011 Past observations have shown that a frequent item set mining algorithm are alleged to mine the closed ones because the finish offers a compact and a whole progress set and higher potency.
Prof. S.Ramakrishna +1 more
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H.: Closed non-derivable itemsets
, 2006 . Itemset mining typically results in large amounts of redundant itemsets. Several approaches such as closed itemsets, non-derivable itemsets and generators have been suggested for losslessly reducing the amount of itemsets.
Hannu Toivonen, Juho Muhonen
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Catch the Moment: Maintaining Closed Frequent Itemsets
, 2008 This paper considers the problem of mining closed frequent itemsets over a data stream sliding window using limited memory space. We design a synopsis data structure to monitor transactions in the sliding window so that we can output the current closed
Philip S. Yu +4 more
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Mining Generalized Closed Frequent Itemsets of Generalized Association Rules
, 2020 . In the area of knowledge discovery in databases, the generalized association rule mining is an extension from the traditional association rule mining by given a database and taxonomy over the items in database. More initiative and informative knowledge
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Interpretable heart disease risk prediction via FCA-constrained logistic regression
Health Informatics JournalObjective To develop an interpretable and clinically coherent heart disease risk prediction model by integrating Formal Concept Analysis (FCA) with a novel closure-constrained logistic regression that enforces coefficient coherence within FCA-derived ...
Arman Salehi +3 more
doaj +1 more source
ItemListFCI：An Algorithm for Mining Closed Frequent Itemsets Based on Bit Table
, 2010 Mining closed frequent itemsets in data streams is an important task in stream data mining. Most of the traditional algorithms for mining closed frequent itemsets are Apriori-based which find the frequent itemsets from large amount of candidates, and ...
Ling Chen, Cai Yan Dai, Ke Ming Tang
core +1 more source