A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm

Lorbeer, Boris; Kosareva, Ana; Deva, Bersant; Softić, Dženan; Ruppel, Peter; Küpper, Axel

doi:10.1007/978-3-319-47898-2_18

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…A comparison between the three algorithm can be seen in Table 1. The multiple Fuzzy-C means have been applied to health data set for medical diagnoses of headache, 28 BIRCH has been applied to cluster data sets of different time points, 24 and Denclue algorithm (Denclue-IM) has been used in spam base data set to classify e-mail as spam or nonspam. 17 Clustering analysis is limited in that there is no one clustering algorithm that works best for all solution.…”

Section: Summary and Recommendationsmentioning

confidence: 99%

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Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Nwadiugwu

2020

Bioinform Biol Insights

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The current study seeks to compare 3 clustering algorithms that can be used in gene-based bioinformatics research to understand disease networks, protein-protein interaction networks, and gene expression data. Denclue, Fuzzy-C, and Balanced Iterative and Clustering using Hierarchies (BIRCH) were the 3 gene-based clustering algorithms selected. These algorithms were explored in relation to the subfield of bioinformatics that analyzes omics data, which include but are not limited to genomics, proteomics, metagenomics, transcriptomics, and metabolomics data. The objective was to compare the efficacy of the 3 algorithms and determine their strength and drawbacks. Result of the review showed that unlike Denclue and Fuzzy-C which are more efficient in handling noisy data, BIRCH can handle data set with outliers and have a better time complexity.

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Section: Summary and Recommendationsmentioning

confidence: 99%

“…BIRCH: A data set showing (A) group of combined clusters, (B) cluster radius and distance, and (C) categories of different clusters with each containing similar elements 24. …”

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confidence: 99%

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Nwadiugwu

2020

Bioinform Biol Insights

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“…Improved BIRCH (Ismael et al 2014) is an extension which uses different distance thresholds per CF which are increased based on entries close to the radius boundary. Similarly, A-BIRCH (Lorbeer et al 2017) estimates the threshold parameters by using the Gap Statistics (Tibshirani et al 2001) on a sample of the stream.…”

Section: Clustering Featurementioning

confidence: 99%

Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Carnein

Trautmann

2019

Bus Inf Syst Eng

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Analyzing data streams has received considerable attention over the past decades due to the widespread usage of sensors, social media and other streaming data sources. A core research area in this field is stream clustering which aims to recognize patterns in an unordered, infinite and evolving stream of observations. Clustering can be a crucial support in decision making, since it aims for an optimized aggregated representation of a continuous data stream over time and allows to identify patterns in large and high-dimensional data. A multitude of algorithms and approaches has been developed that are able to find and maintain clusters over time in the challenging streaming scenario. This survey explores, summarizes and categorizes a total of 51 stream clustering algorithms and identifies core research threads over the past decades. In particular, it identifies categories of algorithms based on distance thresholds, density grids and statistical models as well as algorithms for high dimensional data. Furthermore, it discusses applications scenarios, available software and how to configure stream clustering algorithms. This survey is considerably more extensive than comparable studies, more up-to-date and highlights how concepts are interrelated and have been developed over time.

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“…For instance, air temperature data (T) can be represented using V HT, HT, MT, LT, and VLT. To investigate the effect of different categorization methods in rule mining and visualization, we apply four popular unsupervised strategies to the raw data: equal interval, equal frequency, K-means [46], and birch clustering [47].…”

Section: Data Categorizationmentioning

confidence: 99%

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

Wang

et al. 2018

IJGI

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Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of "The Great Arctic Cyclone of 2012". The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena.

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A-BIRCH: Automatic Threshold Estimation for the BIRCH Clustering Algorithm

Cited by 15 publications

References 11 publications

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Gene-Based Clustering Algorithms: Comparison Between Denclue, Fuzzy-C, and BIRCH

Optimizing Data Stream Representation: An Extensive Survey on Stream Clustering Algorithms

Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data

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