A guided FP-Growth algorithm for mining multitude-targeted item-sets and class association rules in imbalanced data
Identifying frequent item-sets is a popular data-mining task. It consists of finding sets of items
frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets …
frequently appearing in data. Yet, finding all frequent item-sets in large or dense datasets …
A lattice-based approach for mining high utility association rules
T Mai, B Vo, LTT Nguyen - Information Sciences, 2017 - Elsevier
Most businesses focus on the profits. For example, supermarkets often analyze sale
activities to investigate which products bring the most revenue, as well as find out customer …
activities to investigate which products bring the most revenue, as well as find out customer …
Incremental algorithm for association rule mining under dynamic threshold
Data mining is essentially applied to discover new knowledge from a database through an
iterative process. The mining process may be time consuming for massive datasets. A widely …
iterative process. The mining process may be time consuming for massive datasets. A widely …
A new methodology for mining frequent itemsets on temporal data
M Ghorbani, M Abessi - IEEE Transactions on Engineering …, 2017 - ieeexplore.ieee.org
Temporal data contain time-stamping information that affects the results of data mining.
Traditional techniques for finding frequent itemsets assume that datasets are static and the …
Traditional techniques for finding frequent itemsets assume that datasets are static and the …
An efficient algorithm for unique class association rule mining
Association rule mining is one of the main means in Knowledge discovery and Machine
learning. Such kind of rules present knowledge of interrelations among items in a dataset …
learning. Such kind of rules present knowledge of interrelations among items in a dataset …
Using rule-based classifiers in systematic reviews: a semantic class association rules approach
Systematic review is the scientific process that provides reliable answers to a particular
research question by interpreting the current pertinent literature. There is a significant shift …
research question by interpreting the current pertinent literature. There is a significant shift …
ACPRISM: Associative classification based on PRISM algorithm
Associative classification (AC) is an integration between association rules and classification
tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms …
tasks that aim to predict unseen samples. Several studies indicate that the AC algorithms …
Efficient mining of class association rules with the itemset constraint
Mining class association rules (CARs) with the itemset constraint is concerned with the
discovery of rules, which contain a set of specific items in the rule antecedent and a class …
discovery of rules, which contain a set of specific items in the rule antecedent and a class …
Mining minimal high-utility itemsets
Mining high-utility itemsets (HUIs) is a key data mining task. It consists of discovering groups
of items that yield a high profit in transaction databases. A major drawback of traditional high …
of items that yield a high profit in transaction databases. A major drawback of traditional high …
High average-utility itemsets mining: a survey
HUIM (High utility itemsets mining) is a sub-division of data mining dealing with the task to
obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the …
obtain promising patterns in the quantitative datasets. A variant of HUIM is to discover the …