Parallel architecture to accelerate superparamagnetic clustering algorithm

Abstract

Superparamagnetic clustering (SPC) is an unsupervised classification technique in which clusters are self-organised based on data density and mutual interaction energy. Traditional SPC algorithm uses the Swendsen-Wang Monte Carlo approximation technique to significantly reduce the search space for reasonable clustering. However, Swendsen-Wang approximation is a Markov process which limits the conventional superparamagnetic technique to process data clustering in a sequential manner. Here the authors propose a parallel approach to replace the conventional appropriation to allow the algorithm to perform clustering in parallel. One synthetic and one open-source dataset were used to validate the accuracy of this parallel approach in which comparable clustering results were obtained as compared to the conventional implementation. The parallel method has an increase of clustering speed at least 8.7 times over the conventional approach, and the larger the sample size, the more increase in speed was observed. This can be explained by the higher degree of parallelism utilised for the increased data points. In addition, a hardware architecture was proposed to implement the parallel superparamagnetic algorithm using digital electronic technologies suitable for rapid or real-time neural spike sorting.

Research on Big Data Text Clustering Algorithm Based on Swarm Intelligence

“…In formula (10), L i represents the linear sum of all elements in the cluster, and Q i represents the sum of coefficients of all elements in the cluster.…”

“…Algorithm under Swarm Intelligence (1) Swarm intelligence control is distributed, and there is no central control [10]. Therefore, it can better adapt to the working state in the current network environment and has strong robustness; that is, it will not affect the group's solution to the whole problem due to the failure of one or several individuals…”

A Unified Approach for Patient Activity Recognition in Healthcare Using Depth Camera

“…In this real-time body segmentation method, superparamagnetic clustering of data for segmentation is used which determines the equipoise of the states of a Potts model. Superparamagnetic clustering algorithm shows better performance in frame-based classification; however, its accuracy degrades when applied on sequential data [24]. The authors of [25], [26] developed state of the art methods respectively for trajectory classification (using segmental HMM) and facial expression recognition (using deep learning), which might be employed for activity recognition.…”