Classification of data streams has gained a lot of popularity in recent years owing to its multiple applications. In certain applications like community detection from text feeds, website fingerprinting attack, etc., it ...

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, web log analysis, etc. Various algorithms ...

Ease of programming and optimal parallel performance have historically been on the opposite side of a trade-off, forcing the user to choose. With the advent of the Big Data era and the rapid evolution of sequential algorithms, ...

For a search engine, the challenge of finding relevant information from the web is becoming more and more difficult with rapid increase/change in content of the web. This difficulty further increases as queries submitted ...

Parallelizing data mining algorithms has become a necessity as we try to mine ever increasing volumes of data. Spatial data mining algorithms like Dbscan, Optics, Slink, etc. have been parallelized to exploit a cluster ...

Clustering of large volumes of data is a complex problem which requires use of sophisticated algorithms as well as High Performance Computing hardware like a cluster of computers. It is highly desirable that data mining ...

DBSCAN is one of the popular density-based clustering algorithms, but requires re-clustering the entire data when the input parameters are changed. OPTICS overcomes this limitation. In this paper, we propose a batch-wise ...

DBSCAN is one of the most popular density-based clustering algorithm capable of identifying arbitrary shaped clusters and noise. It is computationally expensive for large data sets. In this paper, we present a grid-based ...

Single linkage (SLINK) hierarchical clustering algorithm is a preferred clustering algorithm over traditional partitioning-based clustering as it does not require the number of clusters as input. But, due to its high time ...

Mining large data sets is no longer the prerogative of computer scientists - specialists in a wide variety of domains are performing analytics as a day-to-day activity. Often such analyses are specific to the domain and ...