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High-order possibilistic c-means algorithms based on tensor decompositions for big data in IoT
الگوریتم های c-means احتمالی اولویت بالا بر اساس تجزیه تانسور برای داده های بزرگ در اینترنت اشیا-2018 Internet of Things (IoT) connects the physical world and the cyber world to offer intelligent services by
data mining for big data. Each big data sample typically involves a large number of attributes, posing a
remarkable challenge on the high-order possibilistic c-means algorithm (HOPCM). Specially, HOPCM requires high-performance servers with a large-scale memory and a powerful computing unit, to cluster big samples, limiting its applicability in IoT systems with low-end devices such as portable computing
units and embedded devises which have only limited memory space and computing power. In this paper,
we propose two high-order possibilistic c-means algorithms based on the canonical polyadic decomposition (CP-HOPCM) and the tensor-train network (TT-HOPCM) for clustering big data. In detail, we use the canonical polyadic decomposition and the tensor-train network to compress the attributes of each big data sample. To evaluate the performance of our algorithms, we conduct the experiments on two representative big data datasets, i.e., NUS-WIDE-14 and SNAE2, by comparison with the conventional highorder possibilistic c-means algorithm in terms of attributes reduction, execution time, memory usage and clustering accuracy. Results imply that CP-HOPCM and TT-HOPCM are potential for big data clustering
in IoT systems with low-end devices since they can achieve a high compression rate for heterogeneous
samples to save the memory space significantly without a significant clustering accuracy drop.
Keywords: Big data ، IoT ، Possibilistic c-means clustering ، Canonical polyadic decomposition ، Tensor-train network |
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