We propose a new on-line ε-approximation algorithm for mining closed itemsets from a transactional data stream, which is also based on the incremental/cumulative intersection principle. The proposed algorithm, called LC-CloStream, is constructed by integrating CloStream algorithm and Lossy Counting algorithm. We investigate some behaviors of the LC-CloStream algorithm. Firstly we show the incom...

Some challenges in frequent pattern mining from data streams are the drift of data distribution and the computational efficiency. In this work an additional challenge is considered: data streams describe complex objects modeled by multiple database relations. A multi-relational data mining algorithm is proposed to efficiently discover approximate relational frequent patterns over a sliding time...

Embedded real-time data stream mining is an important branch of data mining. We propose and provide some fundamental prerequisites for using statistical pattern recognition in stream-based learning/decision systems in this paper: namely some real-time kernel functions and a real-time analysis of the support vector machine’s decision function. In this paper we analyze two kernels and a support v...

Data Stream Mining is the evolving field of research. Mining continuous data streams brings unique opportunities but also new challenges. This paper will describe and evaluate the proposed classifier which uses ensemble classifier along with the boosting concept. Adaptive windowing is also used for handling the data stream. Empirical study will show that the proposed classifier takes less memor...

1. Summary. The paper proposes a novel streaming algorithm to mine the top-k episodes in a stream of events. The frequency of the episodes is computed over a sliding window which length is defined by the user. The key idea in this paper is based on two new concepts related to the stream: maximum rate of change and top-k separation. The sliding window is decomposed into batches and the previous ...

Wireless data sharing is the term that facilitates effective and ubiquitous wireless access and affordable mobile devices, so much of the internet applications are assessed in this context. For doing this facilitate effectively traditionally so much of techniques were introduced in recent application development process. Increase of the mobile devices network applications in streaming of this a...

The processes of extracting knowledge structures for continuous, rapid records are known as the Data Stream Mining. The main issue in stream mining is handling streams of elements delivered rapidly which makes it infeasible to store everything in active storage. To overcome this problem of handling voluminous data we exposed a novel load shedding system using window based aggregate function of ...

Data mining is the process of extracting knowledge structures from continuous, rapid and extremely large stream data which handles quality and data analysis. In such traditional transaction environment it is impossible to perform frequent items mining because it requires analyzing which item is a frequent one to continuously incoming stream data and which is probable to become a frequent item. ...

In recent years, stream data have become an immensely growing area of research for the database, computer science and data mining communities. Stream data is an ordered sequence of instances. In many applications of data stream mining data can be read only once or a small number of times using limited computing and storage capabilities. Some of the issues occurred in classifying stream data tha...

Mining frequent itemsets over a stream of transactions presents di cult new challenges over traditional mining in static transaction databases. Stream transactions can only be looked at once and streams have a much richer frequent itemset structure due to their inherent temporal nature. We examine a novel data structure, an FP-stream, for maintaining information about itemset frequency historie...