An Effective Streams Clustering Method for Biomedical Signals

In the healthcare industry, the ability to monitor patients via biomedical signals assists healthcare professionals in detecting early signs of conditions such as blocked arteries and abnormal heart rhythms. Using data clustering, it is possible to interpret these signals to look for patterns that may indicate emerging or developing conditions. This can be accomplished by basing monitoring systems on a fast clustering algorithm that processes fast-paced streams of raw data effectively. This paper presents a clustering method, POD-Clus, which can be useful in computer-aided diagnosis. The proposed method clusters data streams in linear time and outperforms a competing algorithm in capturing changes of clusters in data streams.

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A Methodology for Clustering Transient Biomedical Signals by Variable

International Journal of Computational Models and Algorithms in Medicine ◽

10.4018/jcmam.2012010103 ◽

2012 ◽

Vol 3 (1) ◽

pp. 32-71

Author(s):

Pimwadee Chaovalit

Keyword(s):

Data Streams ◽

Data Clustering ◽

Healthcare Professionals ◽

Clustering Algorithms ◽

Biomedical Signals ◽

Model Based Clustering ◽

Physical Conditions ◽

Cluster Data ◽

Baseline Algorithm ◽

Crucial Part

Biomedical signals which help monitor patients’ physical conditions are a crucial part of the healthcare industry. The healthcare professionals’ ability to monitor patients and detect early signs of conditions such as blocked arteries and abnormal heart rhythms can be accomplished by performing data clustering on biomedical signals. More importantly, clustering on streams of biomedical signals make it possible to look for patterns that may indicate developing conditions. While there are a number of clustering algorithms that perform data streams clustering by example, few algorithms exist that perform clustering by variable. This paper presents POD-Clus, a clustering method which uses a model-based clustering principle and, in addition to clustering by example, also cluster data streams by variable. The clustering result from POD-Clus was superior to the result from ODAC, a baseline algorithm, for both with and without cluster evolutions.

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On Fuzzy Non-Metric Model for Data with Tolerance and its Application to Incomplete Data Clustering

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0571 ◽

2016 ◽

Vol 20 (4) ◽

pp. 571-579 ◽

Cited By ~ 1

Author(s):

Yasunori Endo ◽

◽

Tomoyuki Suzuki ◽

Naohiko Kinoshita ◽

Yukihiro Hamasuna ◽

...

Keyword(s):

Data Clustering ◽

Incomplete Data ◽

Clustering Algorithm ◽

Uncertain Data ◽

Data Sets ◽

Membership Degree ◽

Clustering Methods ◽

Clustering Method ◽

Numerical Examples ◽

Metric Model

The fuzzy non-metric model (FNM) is a representative non-hierarchical clustering method, which is very useful because the belongingness or the membership degree of each datum to each cluster can be calculated directly from the dissimilarities between data and the cluster centers are not used. However, the original FNM cannot handle data with uncertainty. In this study, we refer to the data with uncertainty as “uncertain data,” e.g., incomplete data or data that have errors. Previously, a methods was proposed based on the concept of a tolerance vector for handling uncertain data and some clustering methods were constructed according to this concept, e.g. fuzzyc-means for data with tolerance. These methods can handle uncertain data in the framework of optimization. Thus, in the present study, we apply the concept to FNM. First, we propose a new clustering algorithm based on FNM using the concept of tolerance, which we refer to as the fuzzy non-metric model for data with tolerance. Second, we show that the proposed algorithm can handle incomplete data sets. Third, we verify the effectiveness of the proposed algorithm based on comparisons with conventional methods for incomplete data sets in some numerical examples.

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Implementation of power management system by using data clustering method

Contemporary Engineering Sciences ◽

10.12988/ces.2016.6454 ◽

2016 ◽

Vol 9 ◽

pp. 655-661 ◽

Cited By ~ 1

Author(s):

Soon Ki Eun ◽

Jeong Sik Kim ◽

Seong Gon Choi

Keyword(s):

Power Management ◽

Data Clustering ◽

Management System ◽

Clustering Method ◽

Power Management System ◽

Using Data

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Algorithm to forming a rule base for a fuzzy classifier designed on the basis of the K-means clustering algorithm and the whale optimization algorithm

10.21293/1818-0442-2021-24-1-42-47 ◽

2021 ◽

Vol 24 (1) ◽

pp. 42-47

Author(s):

N. P. Koryshev ◽

◽

I. A. Hodashinsky ◽

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Performance Testing ◽

Real Data ◽

Rule Base ◽

Data Sets ◽

Fuzzy Classifier ◽

Whale Optimization ◽

Clustering Quality ◽

Using Data

The article presents a description of the algorithm for generating fuzzy rules for a fuzzy classifier using data clustering, metaheuristic, and the clustering quality index, as well as the results of performance testing on real data sets.

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An Improving Algorithm Based on SOM Clustering and its Applications

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.655-657.1000 ◽

2013 ◽

Vol 655-657 ◽

pp. 1000-1004

Author(s):

Chen Guang Yan ◽

Yu Jing Liu ◽

Jin Hui Fan

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Network Clustering ◽

Self Organizing Map ◽

Clustering Method ◽

Map Algorithm ◽

Som Neural Network ◽

Simulation Results ◽

Som Clustering ◽

Selection Of

SOM (Self-organizing Map) algorithm is a clustering method basing on non-supervision condition. The paper introduces an improved algorithm based on SOM neural network clustering. It proposes SOM’s basic theory on data clustering. For SOM’s practical problems in applications, the algorithm also improved the selection of initial weights and the scope of neighborhood parameters. Finally, the simulation results in Matlab prove that the improved clustering algorithm improve the correct rate and computational efficiency of data clustering and to make the convergence speed better.

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Analysis of Covid-19 data in India using Data Clustering Algorithm

GEDRAG & ORGANISATIE REVIEW ◽

10.37896/gor33.03/490 ◽

2020 ◽

Vol 33 (03) ◽

Author(s):

Sumitha T ◽

◽

Arun Manicka Raja M ◽

Keyword(s):

Data Clustering ◽

Clustering Algorithm ◽

Using Data

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Clustering Large-Scale Data Based On Modified Affinity Propagation Algorithm

Journal of Artificial Intelligence and Soft Computing Research ◽

10.1515/jaiscr-2016-0003 ◽

2016 ◽

Vol 6 (1) ◽

pp. 23-33 ◽

Cited By ~ 23

Author(s):

Ahmed M. Serdah ◽

Wesam M. Ashour

Keyword(s):

Data Clustering ◽

Large Scale ◽

Clustering Algorithm ◽

Clustering Algorithms ◽

Affinity Propagation ◽

Clustering Method ◽

Data Set ◽

Local Cluster ◽

Large Scale Data ◽

Scale Data

Abstract Traditional clustering algorithms are no longer suitable for use in data mining applications that make use of large-scale data. There have been many large-scale data clustering algorithms proposed in recent years, but most of them do not achieve clustering with high quality. Despite that Affinity Propagation (AP) is effective and accurate in normal data clustering, but it is not effective for large-scale data. This paper proposes two methods for large-scale data clustering that depend on a modified version of AP algorithm. The proposed methods are set to ensure both low time complexity and good accuracy of the clustering method. Firstly, a data set is divided into several subsets using one of two methods random fragmentation or K-means. Secondly, subsets are clustered into K clusters using K-Affinity Propagation (KAP) algorithm to select local cluster exemplars in each subset. Thirdly, the inverse weighted clustering algorithm is performed on all local cluster exemplars to select well-suited global exemplars of the whole data set. Finally, all the data points are clustered by the similarity between all global exemplars and each data point. Results show that the proposed clustering method can significantly reduce the clustering time and produce better clustering result in a way that is more effective and accurate than AP, KAP, and HAP algorithms.

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