Abstract Machine learning (ML) is one of the intelligent methodologies that have shown
promising results in the domains of classification and prediction. One of the expanding areas …

Data Clustering or unsupervised classification is one of the main research area in Data
Mining. Partitioning Clustering involves the partitioning of n objects into k clusters. Many …

In data mining, a rule-based classification approach called Associative Classification (AC)
normally builds accurate classifiers from supervised learning data sets. It extracts “If-Then” …

Predicting a match result is a very challenging task and has its own features. Automatic
prediction of a football match result is extensively studied in last two decades and provided …

A typical predictive approach in data mining that produces If-Then knowledge for decision
making is rule-based classification. Rule-based classification includes a large number of …

One of the known classification approaches in data mining is rule induction (RI). RI
algorithms such as PRISM usually produce If-Then classifiers, which have a comparable …

Rule induction (RI) produces classifiers containing simple yet effective 'If–Then'rules for
decision makers. RI algorithms normally based on PRISM suffer from a few drawbacks …

FP-Growth algorithm is an algorithm of association rules that does not generate a set of
candidate, so it has very high practical value in face of the rapid growth of data volume in …

Association rule mining involves discovering concealed correlations among variables often
from sales transactions to help managers in key business decision involving items shelving …

In the current trend of data science research lot of works concentrated over scaling
conventional data classification algorithms towards handling massive datasets referred as …