In this paper we show the potential of contextual itemset mining in the context of Linked Open Data. Contextual itemset mining extracts frequent associations among items considering background information. In the case of Linked Open Data, the background information is represented by an Ontology defined over the data. Each resulting itemset is specific to a particular context and contexts can be...

Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its application to the transaction (or document) classification and clustering. However, most of the frequent-itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to...

Frequent sets play an important role in many Data Mining tasks that try to search interesting patterns from databases, such as association rules, sequences, correlations, episodes, classifiers and clusters. FrequentItemsets Mining (FIM) is the most well-known techniques to extract knowledge from dataset. In this paper differential privacy aims to get means to increase the accuracy of queries fr...

Frequent itemset mining was initially proposed and has been studied extensively in the context of association rule mining. In recent years, several studies have also extended its application to the transaction (or document) classification and clustering. However, most of the frequent-itemset based clustering algorithms need to first mine a large intermediate set of frequent itemsets in order to...

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected requires effective tools methods address the complexity underlying structure semantic information. We propose method that generates an interpretable multilayered network RDF database. The utilises ...