At this current time, data stream classification plays a key role in big analytics due to its enormous growth. Most of the existing methods used ensemble learning, which is trustworthy but these are not effective face issues learning from imbalanced data, it also supposes that all pre-classified. Another weakness takes long evaluation time when target contains high number features. The main obj...

Novelty detection in data stream mining denotes the identification of new or unknown situations in a stream of data elements flowing continuously in at rapid rate. This work is a first attempt of investigating the anomaly detection task in the (multi-)relational data mining. By defining a data block as the collection of complex data which periodically flow in the stream, a relational pattern ba...

Tatsuya Minegishi, Ayahiko Niimi Graduate School of Systems Information Science, Future University Hakodate Faculty of Systems Information Science, Future University Hakodate (g2109043, niimi]@fun.ac.jp) Abstract Global society has experienced a flood of various types of data, as well as a growing desire to discover and use this information effectively. Moreover, this data is changing in inc...

The rapid development in the e-commerce and distributed computing generates millions of the transaction, continuously. This continues arrival of data is considered as a DataStream. Data mining process for classification needs considerable modification to cope with continuous data. As Mining continues stream of data, conceptually has infinite length, and the class of data may change in sudden or...

Statistical Mining in Data StreamsAnkur Jain Recent years have seen a steady rise of a new class of data management systemscalled Data Stream Management Systems (DSMS). These systems manage rapid, high-volume data-streams with transient relations instead of static data with persistent rela-tions. Data streams are common to applications such as network traffic and transac-<lb...

Large tiles in a database are itemsets with the largest area which is defined as the itemset frequency in the database multiplied by its size. Mining these large tiles is an important pattern mining problem since tiles with a large area describe a large part of the database. In this paper, we introduce the problem of mining top-k largest tiles in a data stream under the sliding window model. We...

During the past decade, stream data mining has been attracting widespread attentions of the experts and the researchers all over the world and a large number of interesting research results have been achieved. Among them, frequent itemset mining is one of main research branches of stream data mining with a fundamental and significant position. In order to further advance and develop the researc...