A Methodology for Clustering Transient Biomedical Signals by Variable

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.

An Effective Streams Clustering Method for Biomedical Signals

2010 ◽  
Vol 1 (3) ◽  
pp. 1-30 ◽  
Author(s):  
Pimwadee Chaovalit
Keyword(s):  
Data Streams ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Healthcare Professionals ◽  
Linear Time ◽  
Monitoring Systems ◽  
Clustering Method ◽  
Biomedical Signals ◽  
Using Data ◽  
Aided Diagnosis

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.

scholarly journals Serial Crystallography with Multi-stage Merging oi 1000’s of Images

2017 ◽  
Author(s):  
Herbert J. Bernstein ◽  
Lawrence C. Andrews ◽  
James Foadi ◽  
Martin R. Fuchs ◽  
Jean Jakoncic ◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithms ◽  
Unit Cell ◽  
Physical Parameters ◽  
Data Sets ◽  
Cell Parameters ◽  
Cell Clustering ◽  
Multi Stage ◽  
Cluster Data ◽  
Serial Crystallography

KAMO and Blend provide particularly effective tools to automatically manage the merging of large numbers of data sets from serial crystallography. The requirement for manual intervention in the process can be reduced by extending Blend to support additional clustering options to increase the sensitivity to differences in unit cell parameters and to allow for clustering of nearly complete datasets on the basis of intensity or amplitude differences. If datasets are already sufficiently complete to permit it, apply KAMO once, just for reflections. If starting from incomplete datasets, one applies KAMO twice, first using cell parameters. In this step either the simple cell vector distance of the original Blend is used, or the more sensitive NCDist, to find clusters to merge to achieve sufficient completeness to allow intensities or amplitudes to be compared. One then uses KAMO again using the correlation between the reflections at the common HKLs to merge clusters in a way sensitive to structural differences that may not perturb the cell parameters sufficiently to make meaningful clusters.Many groups have developed effective clustering algorithms that use a measurable physical parameter from each diffraction still or wedge to cluster the data into categories which can then be merged to, hopefully, yield the electron density from a single protein iso-form. What is striking about many of these physical parameters is that they are largely independent from one another. Consequently, it should be possible to greatly improve the efficacy of data clustering software by using a multi-stage partitioning strategy. Here, we have demonstrated one possible approach to multi-stage data clustering. Our strategy was to use unit-cell clustering until merged data was of sufficient completeness to then use intensity based clustering. We have demonstrated that, using this strategy, we were able to accurately cluster data sets from crystals that had subtle differences.

scholarly journals Empowering Density-based Micro-clusters In Dynamic Data Stream Clustering

2020 ◽  
pp. 259-259
Author(s):  
Asha P. V. ◽  
Anju M. Sukumar
Keyword(s):  
Data Streams ◽  
Data Clustering ◽  
Data Stream ◽  
Clustering Algorithms ◽  
Streaming Data ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Fast Processing ◽  
Geospatial Services ◽  
Mining Data Streams

Data stream is a continuous sequence of data generated from various sources and continuously transferred from source to target. Streaming data needs to be processed without having access to all of the data. Some of the sources generating data streams are social networks, geospatial services, weather monitoring, e-commerce purchases, etc. Data stream mining is the process of acquiring knowledge structures from the continuously arriving data. Clustering is an unsupervised machine learning technique that can be used to extract knowledge patterns from the data stream. The mining of streaming data is challenging because the data is in huge amounts and arriving continuously. So the traditional algorithms are not suitable for mining data streams. Data stream mining requires fast processing algorithms using a single scan and a limited amount of memory. The micro clustering has a good role in this. In itself, density based micro clustering has its own unique place in data stream mining. This paper presents a survey on different data clustering algorithms, realizes and empowers the use of density-based micro clusters.

scholarly journals A General Framework for Mixed and Incomplete Data Clustering Based on Swarm Intelligence Algorithms

Mathematics ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 786
Author(s):  
Yenny Villuendas-Rey ◽  
Eley Barroso-Cubas ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Swarm Intelligence ◽  
Data Clustering ◽  
Incomplete Data ◽  
Missing Values ◽  
Clustering Algorithms ◽  
Bat Algorithm ◽  
Hybrid Features ◽  
Bee Colony ◽  
Learning Tasks ◽  
Clustering Data

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the “natural structure” of data.

Data Clustering Algorithms Using Rough Sets

2013 ◽  
pp. 297-327 ◽  
Author(s):  
B.K. Tripathy ◽  
Adhir Ghosh
Keyword(s):  
Comparative Study ◽  
Rough Set ◽  
Fuzzy Clustering ◽  
Fuzzy Set ◽  
Rough Sets ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Future Studies ◽  
Multiple Clusters

Developing Data Clustering algorithms have been pursued by researchers since the introduction of k-means algorithm (Macqueen 1967; Lloyd 1982). These algorithms were subsequently modified to handle categorical data. In order to handle the situations where objects can have memberships in multiple clusters, fuzzy clustering and rough clustering methods were introduced (Lingras et al 2003, 2004a). There are many extensions of these initial algorithms (Lingras et al 2004b; Lingras 2007; Mitra 2004; Peters 2006, 2007). The MMR algorithm (Parmar et al 2007), its extensions (Tripathy et al 2009, 2011a, 2011b) and the MADE algorithm (Herawan et al 2010) use rough set techniques for clustering. In this chapter, the authors focus on rough set based clustering algorithms and provide a comparative study of all the fuzzy set based and rough set based clustering algorithms in terms of their efficiency. They also present problems for future studies in the direction of the topics covered.

Uncertainty-Based Clustering Algorithms for Large Data Sets

2018 ◽  
pp. 1-33 ◽  
Author(s):  
B. K. Tripathy ◽  
Hari Seetha ◽  
M. N. Murty
Keyword(s):  
Big Data ◽  
Data Clustering ◽  
Clustering Algorithms ◽  
Large Data ◽  
Large Data Sets ◽  
Mining Machine ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
New Algorithms

Data clustering plays a very important role in Data mining, machine learning and Image processing areas. As modern day databases have inherent uncertainties, many uncertainty-based data clustering algorithms have been developed in this direction. These algorithms are fuzzy c-means, rough c-means, intuitionistic fuzzy c-means and the means like rough fuzzy c-means, rough intuitionistic fuzzy c-means which base on hybrid models. Also, we find many variants of these algorithms which improve them in different directions like their Kernelised versions, possibilistic versions, and possibilistic Kernelised versions. However, all the above algorithms are not effective on big data for various reasons. So, researchers have been trying for the past few years to improve these algorithms in order they can be applied to cluster big data. The algorithms are relatively few in comparison to those for datasets of reasonable size. It is our aim in this chapter to present the uncertainty based clustering algorithms developed so far and proposes a few new algorithms which can be developed further.

Projected Clustering for Biological Data Analysis

2011 ◽  
pp. 1617-1622
Author(s):  
Ping Deng ◽  
Qingkai Ma ◽  
Weili Wu
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Biological Data ◽  
High Dimensional ◽  
Projected Clustering ◽  
Cluster Data ◽  
Biological Data Analysis ◽  
Data Points ◽  
Entire Dataset

Clustering can be considered as the most important unsupervised learning problem. It has been discussed thoroughly by both statistics and database communities due to its numerous applications in problems such as classification, machine learning, and data mining. A summary of clustering techniques can be found in (Berkhin, 2002). Most known clustering algorithms such as DBSCAN (Easter, Kriegel, Sander, & Xu, 1996) and CURE (Guha, Rastogi, & Shim, 1998) cluster data points based on full dimensions. When the dimensional space grows higher, the above algorithms lose their efficiency and accuracy because of the so-called “curse of dimensionality”. It is shown in (Beyer, Goldstein, Ramakrishnan, & Shaft, 1999) that computing the distance based on full dimensions is not meaningful in high dimensional space since the distance of a point to its nearest neighbor approaches the distance to its farthest neighbor as dimensionality increases. Actually, natural clusters might exist in subspaces. Data points in different clusters may be correlated with respect to different subsets of dimensions. In order to solve this problem, feature selection (Kohavi & Sommerfield, 1995) and dimension reduction (Raymer, Punch, Goodman, Kuhn, & Jain, 2000) have been proposed to find the closely correlated dimensions for all the data and the clusters in such dimensions. Although both methods reduce the dimensionality of the space before clustering, the case where clusters may exist in different subspaces of full dimensions is not handled well. Projected clustering has been proposed recently to effectively deal with high dimensionalities. Finding clusters and their relevant dimensions are the objectives of projected clustering algorithms. Instead of projecting the entire dataset on the same subspace, projected clustering focuses on finding specific projection for each cluster such that the similarity is reserved as much as possible.

Featureless Data Clustering

2010 ◽  
pp. 141-164 ◽  
Author(s):  
Wilson Wong
Keyword(s):  
Data Clustering ◽  
Dominant Role ◽  
Clustering Algorithms ◽  
Adaptive Clustering ◽  
Feature Based ◽  
Clustering Approach ◽  
Semantic Computation ◽  
The Many ◽  
Time Required ◽  
Existing Data

Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However, the applicability of existing data clustering approaches to a wider range of applications is limited due to issues such as complexity involved in semantic computation, long pre-processing time required for feature preparation, and poor extensibility of semantic measurement due to non-incremental feature source. This chapter first summarises the many commonly used clustering algorithms and feature-based semantic measurements, and then highlights the shortcomings to make way for the proposal of an adaptive clustering approach based on featureless semantic measurements. The chapter concludes with experiments demonstrating the performance and wide applicability of the proposed clustering approach.

A Research Roadmap of Big Data Clustering Algorithms for Future Internet of Things

2019 ◽  
Vol 9 (2) ◽  
pp. 16-30 ◽  
Author(s):  
Hind Bangui ◽  
Mouzhi Ge ◽  
Barbora Buhnova
Keyword(s):  
Big Data ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Future Internet ◽  
Research Challenges ◽  
Initial Stage ◽  
Big Data Technologies

Due to the massive data increase in different Internet of Things (IoT) domains such as healthcare IoT and Smart City IoT, Big Data technologies have been emerged as critical analytics tools for analyzing the IoT data. Among the Big Data technologies, data clustering is one of the essential approaches to process the IoT data. However, how to select a suitable clustering algorithm for IoT data is still unclear. Furthermore, since Big Data technology are still in its initial stage for different IoT domains, it is thus valuable to propose and structure the research challenges between Big Data and IoT. Therefore, this article starts by reviewing and comparing the data clustering algorithms that can be applied in IoT datasets, and then extends the discussions to a broader IoT context such as IoT dynamics and IoT mobile networks. Finally, this article identifies a set of research challenges that harvest a research roadmap for the Big Data research in IoT domains. The proposed research roadmap aims at bridging the research gaps between Big Data and various IoT contexts.

Single-cell RNA-seq data clustering: A survey with performance comparison study

2020 ◽  
Vol 18 (04) ◽  
pp. 2040005
Author(s):  
Ruiyi Li ◽  
Jihong Guan ◽  
Shuigeng Zhou
Keyword(s):  
Single Cell ◽  
Data Clustering ◽  
Performance Metrics ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Performance Comparison ◽  
Cellular Heterogeneity ◽  
Clustering Methods ◽  
Multiple Perspectives ◽  
Underlying Mechanisms

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some of their counterparts usually using different performance metrics. Consequently, there lacks an accurate and complete picture of their merits and demerits, which makes it difficult for users to select proper algorithms for analyzing their data. To fill this gap, we first do a review on the major existing scRNA-seq data clustering methods, and then conduct a comprehensive performance comparison among them from multiple perspectives. We consider 13 state of the art scRNA-seq data clustering algorithms, and collect 12 publicly available real scRNA-seq datasets from the existing works to evaluate and compare these algorithms. Our comparative study shows that the existing methods are very diverse in performance. Even the top-performance algorithms do not perform well on all datasets, especially those with complex structures. This suggests that further research is required to explore more stable, accurate, and efficient clustering algorithms for scRNA-seq data.

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