Pruning strategies for mining high utility itemsets
S Krishnamoorthy - Expert Systems with Applications, 2015 - Elsevier
High utility itemset mining problem involves the use of internal and external utilities of items
(such as profits, margins) to discover interesting patterns from a given transactional …
(such as profits, margins) to discover interesting patterns from a given transactional …
HMiner: Efficiently mining high utility itemsets
S Krishnamoorthy - Expert Systems with Applications, 2017 - Elsevier
High utility itemset mining problem uses the notion of utilities to discover interesting and
actionable patterns. Several data structures and heuristic methods have been proposed in …
actionable patterns. Several data structures and heuristic methods have been proposed in …
Using consumer behavior data to reduce energy consumption in smart homes: Applying machine learning to save energy without lowering comfort of inhabitants
D Schweizer, M Zehnder, H Wache… - 2015 IEEE 14th …, 2015 - ieeexplore.ieee.org
This paper discusses how usage patterns and preferences of inhabitants can be learned
efficiently to allow smart homes to autonomously achieve energy savings. We propose a …
efficiently to allow smart homes to autonomously achieve energy savings. We propose a …
Mining top-k high utility itemsets with effective threshold raising strategies
S Krishnamoorthy - Expert Systems with Applications, 2019 - Elsevier
Abstract Top-K High Utility Itemset (HUI) mining problem offers greater flexibility to a
decision maker in specifying her/his notion of item utility and the desired number of patterns …
decision maker in specifying her/his notion of item utility and the desired number of patterns …
Efficient mining of high utility itemsets with multiple minimum utility thresholds
S Krishnamoorthy - Engineering Applications of Artificial Intelligence, 2018 - Elsevier
Mining high utility itemsets is considered to be one of the important and challenging
problems in the data mining literature. The problem offers greater flexibility to a decision …
problems in the data mining literature. The problem offers greater flexibility to a decision …
[HTML][HTML] Miningzinc: A declarative framework for constraint-based mining
We introduce MiningZinc, a declarative framework for constraint-based data mining.
MiningZinc consists of two key components: a language component and an execution …
MiningZinc consists of two key components: a language component and an execution …
BicSPAM: flexible biclustering using sequential patterns
R Henriques, SC Madeira - BMC bioinformatics, 2014 - Springer
Background Biclustering is a critical task for biomedical applications. Order-preserving
biclusters, submatrices where the values of rows induce the same linear ordering across …
biclusters, submatrices where the values of rows induce the same linear ordering across …
DEOP: a database on osmoprotectants and associated pathways
S Bougouffa, A Radovanovic, M Essack, VB Bajic - Database, 2014 - academic.oup.com
Microorganisms are known to counteract salt stress through salt influx or by the
accumulation of osmoprotectants (also called compatible solutes). Understanding the …
accumulation of osmoprotectants (also called compatible solutes). Understanding the …
An effective association rule mining scheme using a new generic basis
Association rule mining among itemsets is a fundamental task and is of great importance in
many data mining applications including attacks in network data, stock market, financial …
many data mining applications including attacks in network data, stock market, financial …
An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix
MK Sohrabi - Knowledge and Information Systems, 2020 - Springer
High utility itemset mining is an important extension of frequent itemset mining which
considers unit profits and quantities of items as external and internal utilities, respectively …
considers unit profits and quantities of items as external and internal utilities, respectively …