An Adaptive Density Data Stream Clustering Algorithm

Abstract

Now we are in the age of big data. Huge amount of data and information are generated every time. Traditional data stream algorithms are suit for the data streams with low dimension and simple structure. However, with the development of information technology, the produced data streams are becoming more and more complicated. It is particularly important to study how to find new associations and patterns from complex data to achieve the cognition ability and judgment ability like human brain. Clustering data streams with mixed attributes of irregular distribution is a big challenge in data mining. To solve this problem, we present an adaptive density data stream clustering algorithm-ADStream. ADStream is based on the online-off-line clustering framework. It can automatically recognize the initial clusters by passing messages between data points. Then a novel time-decay density clustering strategy is designed to group and update the continuously arriving data streams. Comprehensive experimental results demonstrate that ADStream is adaptive to the evolving data streams and may generate highquality clusters with fast processing rate.

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

“…Traditional typical spatial clustering algorithms that repeatedly access entire data sets cannot be readily applied for data streaming, as their high complexity and computational cost makes it impossible for them to manage such a large amount of data. In fact, data stream clustering algorithms have become important in data mining research and subsequently, many algorithms based on data stream technology have been proposed, including the commonly-used one-pass algorithm [9,[44][45][46][47][48]. This algorithm divides the non-streaming data sets into data blocks so as to fit requirements of the memory space and one-pass sweeping data objects.…”

“…If the parameters are set without a priori knowledge (or measured experimental results), it is difficult to find true clusters accurately. based on data stream technology have been proposed, including the commonly-used one-pass algorithm [9,[44][45][46][47][48]. This algorithm divides the non-streaming data sets into data blocks so as to fit requirements of the memory space and one-pass sweeping data objects.…”

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

“…Traditional typical spatial clustering algorithms that repeatedly access entire data sets cannot be readily applied for data streaming, as their high complexity and computational cost makes it impossible for them to manage such a large amount of data. In fact, data stream clustering algorithms have become important in data mining research and subsequently, there have been many algorithms based on data stream technology proposed, including the commonly-used one-pass algorithm [9,[44][45][46][47][48]. This algorithm divides the non-streaming data sets into data blocks so as to fit requirements of the memory space and one-pass sweeping data objects.…”

An Adaptive Sweep-circle Spatial Clustering Algorithm Based on Gestalt

“…In fact, data stream clustering algorithms have become important from within data mining research and there have been many algorithms based on data stream technology proposed, including the commonly-used one-pass algorithm [44] [9] [45][46][47][48]. This algorithm divides the non-stream data sets into data blocks so as to fit requirements of the memory space and one-pass sweeping data objects.…”