As we are living in the era of big data, high volumes of wide varieties of data which may be
of different veracity (eg, precise data, imprecise and uncertain data) are easily generated or …

Mining frequent itemsets from transactional data streams has become very essential in
today's world with many applications such as stock market analysis, retail chain analysis …

Itemset mining is a popular extension to the frequent pattern mining problem in data mining.
Finding infrequent patterns, however, has gained its importance due to proven utility in the …

Clustering of data streams has become very popular in recent times, owing to rapid rise of
real-time streaming utilities that produce large amounts of data at varying inter-arrival rates …

Stream Clustering algorithms have gained a lot of importance in the recent past due to rapid
rising utilities of IoT systems and applications. Anytime algorithms and frameworks play a …

The streams where multiple transactions are associated with the same key are prevalent in
practice, eg, a customer has multiple shop** records arriving at different time. Itemset …

Frequent pattern mining is a useful data mining technique. It can help in digging out
frequently used patterns from the massive internet data streams for significant applications …

During the past decades, information technology becomes ever more prevalent in nearly
every aspect of our daily lives, such as medical biology, finance, and scientific research. The …