Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach

Balouchestani, Mohammadreza; Krishnan, Sridhar

doi:10.1007/s11760-014-0709-5

Cited by 30 publications

(12 citation statements)

References 34 publications

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“…Advantage of this algorithm is simplicity and easy implementation. It has limitation of being affected by outliers and noise [12] [13].…”

Section: Background Of Studymentioning

confidence: 99%

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Anitha¹

2016

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Web log mining is analysis of web log files with web page sequences. Discovering user access patterns from web access are necessary for building adaptive web servers, to improve e-commerce, to carry out cross-marketing, for web personalization, to predict web access sequence etc. In this paper, a new agglomerative clustering technique is proposed to identify users with similar interest, and to determine the motivation for visiting a website. Using this approach, web usage mining is done through different stages namely data cleaning, preprocessing, pattern discovery and pattern analysis. Results are given to explain how this approach produces tight usage clusters than the existing web usage mining techniques. Rather than traditional distance based clustering, the similarity measure is considered during clustering process in order to reduce computational complexity. This paper also deals with the problem of assessing the quality of user session clusters and cluster validity is measured by using statistical test, which measures the distances of clusters distributions to infer their dissimilarity and distinguish level. Using such statistical measures, it is proved that cluster accuracy is improved to the extent of 0.83, over existing k-means clustering with validity measure 0.26, FCM (Fuzzy C Means) clustering with validity measure 0.56. Rough set based clustering with validity measure 0.54 Generation of dense clusters is essential for finding interesting patterns needed for further mining and analysis.

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“…Advantage of this algorithm is simplicity and easy implementation. It has limitation of being affected by outliers and noise [12] [13].…”

Section: Background Of Studymentioning

confidence: 99%

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Anitha¹

2016

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“…Compared with predefined dictionaries, learning dictionaries, such as K-SVD [16], [17] and online learning method (OLM) [18], can obtain a compressed model by training the signal characteristics, leading to better sparse representation performance for the original signal. Souza et al [19] and Zhao et al [20] used the K-SVD learning dictionary to compress the electrocardiogram (ECG) signal and forest ecological data, reducing energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Ecological Big Data Adaptive Compression Method Combining 1D Convolutional Neural Network and Switching Idea

et al. 2020

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To address large data transmission requirements and high transmission power consumptions characterizing micro-environment monitoring systems that are commonly used in forest health and safety applications, we propose an ecological big data adaptive switching compression method based on a 1D convolutional neural network (1D CNN). First, to ensure that data samples apply to different compression dictionaries, a 1D CNN is used to classify the samples into two sets according to the characteristics of samples. Subsequently, based on the classification results, the switching factor S is defined, such that the discrete cosine transform (DCT) predefined dictionary and the learning dictionary (K-SVD) can be used to adaptively achieve sparse expression and data compression. Finally, the orthogonal matching pursuit (OMP) algorithm reconstructs the sparse signal. To evaluate the feasibility and robustness of the proposed method, we conduct experiments on four types of data: air temperature (AT), air humidity (AH), soil temperature (ST), and soil humidity (SH). The results indicate that the proposed method, compared to K-SVD and DCT dictionary, exhibits excellent performance for all data samples having smaller sparse error (SE), smaller reconstruction error (RE), and larger compression ratio (CR) at different sparsity levels. In particular, when sparsity K is 16, the reconstructed signal is the most similar to the original signal. In addition, the proposed method reduces power consumption by 79.90%, compared with uncompressed data transmission. Considering four factors, the adaptive switching compression method based on 1D CNN has higher reconstruction accuracy and lower power consumption than using only K-SVD or DCT dictionary.

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“…Specifically, LSA represents the relationship between documents and terms by a term-document matrix that is further decomposed into a product of three other matrices by the singular value decomposition (SVD) [ 1 , 3 , 4 ]. SVD is the mathematical tool behind LSA and some applications including association prediction [ 21 ], similarity computation [ 22 , 23 ], clustering [ 24 , 25 ], images analysis [ 26 ] and collaborative filtering [ 27 , 28 ]. For the given query, LSA transforms it into a pseudo document vector and computes the similarities between query and candidate documents over the SVD result of the term-document matrix.…”

Section: Introductionmentioning

confidence: 99%

An index-based algorithm for fast on-line query processing of latent semantic analysis

Zhang

Wang

2017

PLoS ONE

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Latent Semantic Analysis (LSA) is widely used for finding the documents whose semantic is similar to the query of keywords. Although LSA yield promising similar results, the existing LSA algorithms involve lots of unnecessary operations in similarity computation and candidate check during on-line query processing, which is expensive in terms of time cost and cannot efficiently response the query request especially when the dataset becomes large. In this paper, we study the efficiency problem of on-line query processing for LSA towards efficiently searching the similar documents to a given query. We rewrite the similarity equation of LSA combined with an intermediate value called partial similarity that is stored in a designed index called partial index. For reducing the searching space, we give an approximate form of similarity equation, and then develop an efficient algorithm for building partial index, which skips the partial similarities lower than a given threshold θ. Based on partial index, we develop an efficient algorithm called ILSA for supporting fast on-line query processing. The given query is transformed into a pseudo document vector, and the similarities between query and candidate documents are computed by accumulating the partial similarities obtained from the index nodes corresponds to non-zero entries in the pseudo document vector. Compared to the LSA algorithm, ILSA reduces the time cost of on-line query processing by pruning the candidate documents that are not promising and skipping the operations that make little contribution to similarity scores. Extensive experiments through comparison with LSA have been done, which demonstrate the efficiency and effectiveness of our proposed algorithm.

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Advanced K-means clustering algorithm for large ECG data sets based on a collaboration of compressed sensing theory and K-SVD approach

Cited by 30 publications

References 34 publications

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

An Efficient Agglomerative Clustering Algorithm for Web Navigation Pattern Identification

Ecological Big Data Adaptive Compression Method Combining 1D Convolutional Neural Network and Switching Idea

An index-based algorithm for fast on-line query processing of latent semantic analysis

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