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 ...

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 ...

Conventional coal-based thermal power plants have an average overall efficiency in the range of 35-38 %. Any increase in the percent efficiency of these power plants, is subjected to constraints posed by maximum and minimum ...

Data cleaning is an essential step in populating and maintaining data warehouses. Owing to likely differences in conventions between the external sources and the target data warehouse, as well as due to a variety of errors, ...

In this paper, we propose an algorithm, DOPTICS, a parallelized version of a popular density based cluster-ordering algorithm OPTICS. Parallelizing OPTICS is challenging because of its strong sequential data access behavior. ...

DBSCAN is one of the most popular and effective clustering algorithms that is capable of identifying arbitrary-shaped clusters and noise efficiently. However, its super-linear complexity makes it infeasible for applications ...