Abstract. Unsupervised identification of groups in large data sets is important for many machine learning and knowledge discovery applications. Conventional clustering approaches (kmeans, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal efficiently with potentially unbounded ...

The data stream model has been defined for new classes of applications involving massive data being generated at a fast pace. Web click stream analysis and detection of network intrusions are two examples. Cluster analysis on data streams becomes more difficult, because the data objects in a data stream must be accessed in order and can be read only once or few times with limited resources. Rec...

Data stream is continuous flow of data, which necessitates load shedding for data stream processing system. Here we study overload handling for frequent pattern mining indata streams. Here in this paper load shedding use frequent pattern matching algorithm i.e priority, transaction and attribute in overload situation. The heavy workload or continues stream of the mining algorithm lies mostly in...

In this paper we introduce the ISW (Interval Sliding Window) algorithm, which is applicable to numerical time series data streams and uses as input the combined Hoeffding bound confidence level parameter rather than the maximum error threshold. The proposed algorithm has two advantages: first, it allows performance comparisons across different time series data streams without changing the algor...

Nowadays the data telecommunication security has been provided by most of well-known stream cipher algorithms which are already implemented in different secure protocols such as GSM, SSL, TLS, WEP, Bluetooth etc. These algorithms are A5/1, A5/2, E0 and RC4. On the other hand, these public algorithms already faced to serious security weakness such that they do not provide enough security of prop...

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...

Evolutionary algorithms (EAs) have been well acknowledged as a promising paradigm for solving optimisation problems with multiple conflicting objectives in the sense that they are able to locate a set of diverse approximations of Pareto optimal solutions in a single run. EAs drive the search for approximated solutions through maintaining a diverse population of solutions and by recombining prom...

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...