Online clustering algorithms play a critical role in data science, especially with the advantages regarding time, memory usage and complexity, while maintaining a high performance compared to traditional clustering methods. This tutorial serves, first, ...

This paper presents DeepStream, a novel data stream temporal clustering algorithm that dynamically detects sequential and overlapping clusters. DeepStream is tuned to classify contextual information in real time and is capable of coping with a high-...

A major challenge in stream clustering is the evolution in the statistical properties of the underlying data. As clustering is inherently unsupervised, selecting suitable parameter values is often difficult. Clustering algorithms with sensitive ...

In this paper, SED-Stream-AP is proposed as an extension SED-Stream which is an efficient evolution-based stream clustering technique. SED-Steam-AP is a stream clustering technique that integrates evolution and affinity propagation clustering. It adopts ...

A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. A core element of CPS ...

Natural calamities and man-made hazards can occur in an unexpected and unanticipated manner. They cause large-scale damage, create disruptions, and need instant reaction. In the event of sudden onset of a crisis, rapid formulation of a notification ...

The explosive growth of data generation, storage and analysis within the last decade has led to extensive research towards stream mining algorithms. The existing stream clustering literature contains both adaptation of classical methods as well as novel ...

Nowadays many applications need to deal with <em>evolving data streams</em> . In this work, we propose an incremental clustering approach for the exploitation of user constraints on data streams. Conventional constraints do not make sense on streaming ...

Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon the primary objects (patients, ...

Historical transaction data are collected in many applications, e.g., patient histories recorded by physicians and customer transactions collected by companies. An important question is the learning of models upon the primary objects (patients, ...

Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any ...

Clustering streaming data requires algorithms which are capable of updating clustering results for the incoming data. As data is constantly arriving, time for processing is limited. Clustering has to be performed in a single pass over the incoming data ...

Data stream clustering has emerged as a challenging and interesting problem over the past few years. Due to the evolving nature, and one-pass restriction imposed by the data stream model, traditional clustering algorithms are inapplicable for stream ...