In this paper we propose a data stream clustering algorithm, called Self Organizing density based clustering over data Stream (SOStream). This algorithm has several novel features. Instead of using a fixed, user defined similarity threshold or a static grid, SOStream detects structure within fast evolving data streams by automatically adapting the threshold for density-based clustering. It also...

Most density based stream clustering algorithms separate the clustering process into an online and offline component. Exact summarized statistics are being employed for defining micro-clusters or grid cells during the online stage followed by macro-clustering during the offline stage. This paper proposes a novel alternative to the traditional two phase stream clustering scheme, introducing sket...

Clustering is a challenging topic in the area of Web data management. Various forms of clustering are required in a wide range of applications, including finding mirrored Web pages, detecting copyright violations, and reporting search results in a structured way. Clustering can either be performed once offline, (independently to search queries), or online (on the results of search queries). Imp...

The growth of online transactions coincides with the rise of cyber-criminals’ intent on stealing consumers’ personal and financial data. This fosters fear of online identity theft (FOIT), which in turn may lead to changes in consumer behavior and negatively affect e-business performance. This research aims to identify empirically derived segments of FOIT-prone consumers. Using a large sample of...

Abstract: This paper describes a novel non-linear modelling approach by online clustering, fuzzy rules and support vector machine. Structure identification is realised by an online clustering method and fuzzy support vector machines, and the fuzzy rules are generated automatically. Time-varying learning rates are applied for updating the membership functions of the fuzzy rules. Finally, the upp...

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. To improve sustainability in Smart Grids, realtime data analytics challenges induced by high volume and high dimensional context-based data need to be addressed. Particularly, with the em...

Model-based clustering is a popular tool which is renowned for its probabilistic foundations and its flexibility. However, model-based clustering techniques usually perform poorly when dealing with high-dimensional data streams, which are nowadays a frequent data type. To overcome this limitation of model-based clustering, we propose an online inference algorithm for the mixture of probabilisti...

The ever increasing activity in social networks is mainly manifested by a growing stream of status updating or microblogging. The massive stream of updates emphasizes the need for accurate and efficient clustering of short messages on a large scale. Applying traditional clustering techniques is both inaccurate and inefficient due to sparseness. This paper presents an accurate and efficient algo...

Graph is an extremely useful representation of a wide variety of practical systems in data analysis. Recently, with the fast accumulation of stream data from various type of networks, significant research interests have arisen on spectral clustering for network streams (or evolving networks). Compared with the general spectral clustering problem, the data analysis of this new type of problems m...

Social media becomes a vital part in our daily communication practice, creating a huge amount of data and covering different real-world situations. Currently, there is a tendency in making use of social media during emergency management and response. Most of this effort is performed by a huge number of volunteers browsing through social media data and preparing maps that can be used by professi...