Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, t...

Aligning coordinated text streams from multiple sources and multiple languages has opened many new research venues on cross-lingual knowledge discovery. In this paper we aim to advance state-of-the-art by: (1). extending coarse-grained topic-level knowledge mining to fine-grained information units such as entities and events; (2). following a novel “Datato-Network-to-Knowledge (D2N2K)” paradigm...

Revolution in digitized technologies has made it possible to acquire data on-line in the form of data streams, which are continuous and infinite in nature. Multiple applications varying from critical scientific applications to business and financial applications generate transient data. Since streaming data is ordered sequence of continuously growing unlabeled data instances, it is not feasible...

Multi-Pass Models: It is common in graph mining to consider algorithms that may take more than one pass over the stream. There has also been work in the W-Stream model in which the algorithm is allowed to write to the stream during each pass [9]. These annotations can then be utilized by the algorithm during successive passes and it can be shown that this gives sufficient power to the model for...

A contrast pattern, also known as an emerging pattern [7], is an itemset whose frequency differs significantly between two classes of data. Such patterns describe differences between datasets and have been shown to be useful for building powerful classifiers [11, 9, 2, 8] . Incrementally mining them in changing data is very important, where transactions can be inserted and deleted and mining ne...

Knowledge embedded in a data stream is likely to be changed as time goes by. Identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most mining algorithms over a data stream are not able to extract the recent change of knowledge in a data stream adaptively. This is because the obsolete information of old data element...

Data mining refers to the process of revealing unknown and potentially useful information from a large database. Frequent itemsets mining is one of the foundational problems in data mining, which is to discover the set of products that purchased frequently together by customers from a transaction database. However, there may be a large number of patterns generated from database, and many of the...

-Classification and analysis of data streams are the most promising fields of research and development in Data stream mining. Ensemble based classification approach is one the most challenging flavor of developing an efficient classifier due to large number available base classifiers and increase in the computational time required for training and classification. This research emphasizes on dev...

Data streams are sequence of data examples that continuously arrive at time-varying and possibly unbound streams. These data streams are potentially huge in size and thus it is impossible to process many data mining techniques (e.g., sensor readings, call records, web page visits). Tachiniques for classification fail to successfully process data streams because of two factors: their overwhelmin...