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Journal articles on the topic 'Clustering analysi'

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Published: 10 March 2023

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1

Jadhav, Priyanka, and Rasika Patil. "Analysis of Clustering technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2422–24. http://dx.doi.org/10.31142/ijtsrd15616.

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Manjunath, Mohith, Yi Zhang, Yeonsung Kim, Steve H. Yeo, Omar Sobh, Nathan Russell, Christian Followell, Colleen Bushell, Umberto Ravaioli, and Jun S. Song. "ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data." PeerJ Computer Science 4 (May 21, 2018): e155. http://dx.doi.org/10.7717/peerj-cs.155.

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Background Clustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking. Methods ClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering validation measures are also presented to aid the user in selecting a suitable algorithm for their dataset. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides tutorials that demonstrate potential pitfalls of each algorithm. Conclusions The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner. The validation measures facilitate the process of choosing a suitable clustering algorithm among the available options. ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.

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Fisher, D. "Iterative Optimization and Simplification of Hierarchical Clusterings." Journal of Artificial Intelligence Research 4 (April 1, 1996): 147–78. http://dx.doi.org/10.1613/jair.276.

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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts -- often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to `externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.

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Patel, Khushbu. "Analysis of Various Database Using Clustering Techniques." Global Journal For Research Analysis 3, no. 7 (June 15, 2012): 59–60. http://dx.doi.org/10.15373/22778160/july2014/20.

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Davidson, Ian, and S. S. Ravi. "Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3733–40. http://dx.doi.org/10.1609/aaai.v34i04.5783.

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We explore the area of fairness in clustering from the different perspective of modifying clusterings from existing algorithms to make them fairer whilst retaining their quality. We formulate the minimal cluster modification for fairness (MCMF) problem where the input is a given partitional clustering and the goal is to minimally change it so that the clustering is still of good quality and fairer. We show using an intricate case analysis that for a single protected variable, the problem is efficiently solvable (i.e., in the class P) by proving that the constraint matrix for an integer linear programming (ILP) formulation is totally unimodular (TU). Interestingly, we show that even for a single protected variable, the addition of simple pairwise guidance (to say ensure individual level fairness) makes the MCMF problem computationally intractable (i.e., NP-hard). Experimental results on Twitter, Census and NYT data sets show that our methods can modify existing clusterings for data sets in excess of 100,000 instances within minutes on laptops and find as fair but higher quality clusterings than fair by design clustering algorithms.

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VEGA-PONS, SANDRO, and JOSÉ RUIZ-SHULCLOPER. "A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 03 (May 2011): 337–72. http://dx.doi.org/10.1142/s0218001411008683.

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Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.

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Madhuri, K., and Mr K. Srinivasa Rao. "Social Media Analysis using Optimized K-Means Clustering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 953–57. http://dx.doi.org/10.31142/ijtsrd21558.

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Li, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, and Jianxin Wang. "ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040009. http://dx.doi.org/10.1142/s0219720020400090.

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Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php

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Wang, Xing, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao, and Maozu Guo. "Multiple Independent Subspace Clusterings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5353–60. http://dx.doi.org/10.1609/aaai.v33i01.33015353.

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Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it’s still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets.

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Kerdprasop, Nittaya, Kacha Chansilp, and Kittisak Kerdprasop. "Greenness Pattern Analysis with the Remote Sensing Index Clustering." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 181–86. http://dx.doi.org/10.18178/ijmlc.2017.7.6.643.

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Regal, Andres. "Logistic Profile Generation via Clustering Analy." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 207–12. http://dx.doi.org/10.18178/ijmlc.2020.10.1.921.

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Li, Bin, Jin Yang, Cai Ming Liu, Jian Dong Zhang, and Yan Zhang. "Research on Improved Clustering Algorithm on Web Usage Mining Based on Scientific Analysis of Web Materials." Applied Mechanics and Materials 63-64 (June 2011): 863–67. http://dx.doi.org/10.4028/www.scientific.net/amm.63-64.863.

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Clustering analysis is an important method to research the Web user’s browsing behavior and identify the potential customers on Web usage mining. The traditional user clustering algorithms are not quite accurate. In this paper, we give two improved user clustering algorithms, which are based on the associated matrix of the user’s hits in the process of browsing website. To this matrix, an improved Hamming distance matrix is generated by defining the minimum norm or the generalized relative Hamming distance between any two vectors. Then, similar user clustering are obtained by setting the threshold value. At the last step of our algorithm, the clustering results are confirmed by defining the clustering’s Similar Index and setting sub-algorithm. Finally, the testing examples show that the new algorithms are more accurate than the old one, and the real log data presents that the improved algorithms are practical.

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Bhawsar, Subham, and Aastha Hajari. "Performance Analysis of K-mean Clustering Map for Different Nodes." International Journal of Trend in Scientific Research and Development Volume-2, Issue-6 (October 31, 2018): 1215–17. http://dx.doi.org/10.31142/ijtsrd18859.

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Cleva, C., C. Cachet, and D. Cabrol-Bass. "Clustering of infrared spectra with Kohonen networks." Analusis 27, no. 1 (January 1999): 81–90. http://dx.doi.org/10.1051/analusis:1999111.

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Harish, N. J., and H. S. Manjunatha Reddy. "Robustness Performance Analysis of OEERS Clustering Technique for Heterogeneous Network Models." Indian Journal of Science and Technology 14, no. 42 (November 10, 2021): 3177–89. http://dx.doi.org/10.17485/ijst/v14i42.714.

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Golpîra, Hêriş. "A Hybrid Clustering Method Using Balanced Scorecard and Data Envelopment Analysis." International Journal Of Innovation And Economic Development 1, no. 7 (2015): 15–25. http://dx.doi.org/10.18775/ijied.1849-7551-7020.2015.17.2002.

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This paper introduces a new hybrid clustering method using Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methods. DEA cannot identify its’ input and output itself, and it is a major weakness of the DEA. In the proposed method, this gap is resolved by integrating DEA with BSC. Some decision-making units (DMUs) needed in DEA method, in compliance with some inputs and outputs is the major drawback of this integration. To deal with this disadvantage, the proposed method selects the most important strategic factors, attained from the BSC method. These data considered to be the input data for the DEA method to calculate relative closeness (RC) of each DMU to the ideal one. Plotting the screen diagram regarding RC index leads us to the final clustering method. Finally, computational results show the applicability and usefulness of the method.

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Costa, António C., J. A. Tenreiro Machado, and Maria Dulce Quelhas. "Multidimensional Scaling Applied to Histogram-Based DNA Analysis." Comparative and Functional Genomics 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/289694.

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This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendallτrank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

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Pardede, Timbul. "KAJIAN METODE BERBASIS MODEL PADA ANALISIS KELOMPOK DENGAN PERANGKAT LUNAK MCLUST." Jurnal Matematika Sains dan Teknologi 14, no. 2 (August 15, 2013): 84–100. http://dx.doi.org/10.33830/jmst.v14i2.378.2013.

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Ward method and K-mean method are clustering method in which grouping only base on distance measure among observed objects, without considering statistical aspects. Model-based clustering is a method that use statistical aspects, as its theoretical basis i.e. probability maximum criterion. This model has tenmodels with a variety of geometrical characteristics. Data partition is conducted by utilizing EM (expectation-maximization) algorithm. Then by using Bayesian Information Criterion (BIC) the best model is obtained. This research aimed to assess the effectiveness of ten models from the model-based clustereng and then tocompare result of grouping methods between model-based clustering with Ward clustering and K-mean clustering. This study used simulated data and applied data. Simulated data are generated with the R programs versions 2.14.1. Proses analysis was performed by using the Mclust programs vesions 4.0 with an interface the R programs versions 2.14.1. The results showed that model-based clustering was more effective in separating the condition of one separate group and two overlap groups than ward clustering and K-mean clustering. Metode Ward dan metode K-rataan adalah metode kelompok yang teknik-teknik pengelompokannya hanya memperhatikan ukuran jarak antar objek-objek pengamatan tanpa mempertimbangkan aspek statistiknya. Metode kelompok berbasis model adalah metode kelompok yang didasarkan pada aspek statistik, yaitu kriteria kemungkinan maksimum. Metode kelompok berbasis model mempunyai sepuluh model dengan berbagai macam sifat geometris. Penyekatan data dilakukan dengan menggunakan algoritma Ekspektasi-Maksimum (EM), kemudian dengan pendekatan Bayesian Information Criterion (BIC) diperoleh model terbaik. Penelitian ini bertujuan untuk mengkaji efektivitas dari sepuluh metode berbasis model dan kemudian membandingkan hasil pengelompokannya dengan metode Ward dan metode K-rataan. Penelitian ini menggunakan data simulasi yang dibangkitkan melali program R versi 2.14.1 dan dianalisis dengan menggunakan program Mclust versi 4.0 dengan interface program R. Hasil penelitian menunjukkan bahwa metode kelompok berbasis model lebih efektif memisahkan kelompok-kelompok yang saling tumpang tindih dibandingkan dengan metode gerombol Ward dan K-rataan.

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Rani, Yogita, Manju, and Harish Rohil. "Comparative Analysis of BIRCH and CURE Hierarchical Clustering Algorithm using WEKA 3.6.9." SIJ Transactions on Computer Science Engineering & its Applications (CSEA) 02, no. 01 (February 10, 2014): 25–29. http://dx.doi.org/10.9756/sijcsea/v2i1/0201080201.

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Piovesan, Rebecca, Maria Chiara Dalconi, Lara Maritan, and Claudio Mazzoli. "X-ray powder diffraction clustering and quantitative phase analysis on historic mortars." European Journal of Mineralogy 25, no. 2 (June 12, 2013): 165–75. http://dx.doi.org/10.1127/0935-1221/2013/0025-2263.

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Jessica Quach, Jessica Quach, and Reza Malekian Jessica Quach. "Exploring the Weather Impact on Bike Sharing Usage Through a Clustering Analysis." 電腦學刊 33, no. 5 (October 2022): 163–73. http://dx.doi.org/10.53106/199115992022103305014.

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<p>Bike sharing systems (BSS) have been a popular traveling service for years and are used worldwide. It is attractive for cities and users who wants to promote healthier lifestyles; to reduce air pollution and greenhouse gas emission as well as improve traffic. One major challenge to docked bike sharing system is redistributing bikes and balancing dock stations. Some studies propose models that can help forecasting bike usage; strategies for rebalancing bike distribution; establish patterns or how to identify patterns. Other studies propose to extend the approach by including weather data. This study aims to extend upon these proposals and opportunities to explore how and in what magnitude weather impacts bike usage. Bike usage data and weather data are gathered for the city of Washington D.C. and are analyzed using k-means clustering algorithm. K-means managed to identify three clusters that correspond to bike usage depending on weather conditions. The results show that the weather impact on bike usage was noticeable between clusters. It showed that temperature followed by precipitation weighted the most, out of five weather variables.</p> <p> </p>

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Múnera S., Luis Eduardo. "Q-analysis clustering." Sistemas y Telemática 8, no. 15 (November 25, 2010): 67. http://dx.doi.org/10.18046/syt.v8i15.1022.

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Prades, José, Gonzalo Safont, Addisson Salazar, and Luis Vergara. "Estimation of the Number of Endmembers in Hyperspectral Images Using Agglomerative Clustering." Remote Sensing 12, no. 21 (November 1, 2020): 3585. http://dx.doi.org/10.3390/rs12213585.

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Many tasks in hyperspectral imaging, such as spectral unmixing and sub-pixel matching, require knowing how many substances or materials are present in the scene captured by a hyperspectral image. In this paper, we present an algorithm that estimates the number of materials in the scene using agglomerative clustering. The algorithm is based on the assumption that a valid clustering of the image has one cluster for each different material. After reducing the dimensionality of the hyperspectral image, the proposed method obtains an initial clustering using K-means. In this stage, cluster densities are estimated using Independent Component Analysis. Based on the K-means result, a model-based agglomerative clustering is performed, which provides a hierarchy of clusterings. Finally, a validation algorithm selects a clustering of the hierarchy; the number of clusters it contains is the estimated number of materials. Besides estimating the number of endmembers, the proposed method can approximately obtain the endmember (or spectrum) of each material by computing the centroid of its corresponding cluster. We have tested the proposed method using several hyperspectral images. The results show that the proposed method obtains approximately the number of materials that these images contain.

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GAN, Feng, Qingsong XU, Lin ZHANG, and Yizeng LIANG. "An Improved Optimization Strategy and Its Application to Clustering Analysis." Analytical Sciences 17, no. 7 (2001): 869–73. http://dx.doi.org/10.2116/analsci.17.869.

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Charalampidis, Dimitrios, and Barry Muldrey. "Clustering using multilayer perceptrons." Nonlinear Analysis: Theory, Methods & Applications 71, no. 12 (December 2009): e2807-e2813. http://dx.doi.org/10.1016/j.na.2009.06.064.

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Kenouch, Samir, Dalal Harkati, Myriem Ghamri, A. Rahime Chikhaoui, and Nadjib Melkemi. "Predictive QSAR model and clustering analysis of some Benzothiazole derivatives as cytotoxic inhibitors." SDRP Journal of Computational Chemistry & Molecular Modelling 2, no. 3 (2018): 1–8. http://dx.doi.org/10.25177/jccmm.2.3.3.

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TSUJI, Masatsugu, Shoichi MIYAHARA, Yasushi UEKI, and Somrote KOMOLAVANIJ. "An Empirical Analysis of Industrial Clustering and Technology Transfers in Greater Bangkok, Thailand." International Conference on Business & Technology Transfer 2006.3 (2006): 46–51. http://dx.doi.org/10.1299/jsmeicbtt.2006.3.0_46.

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Armano, Giuliano, and Mohammad Reza Farmani. "Clustering Analysis with Combination of Artificial Bee Colony Algorithm and k-Means Technique." International Journal of Computer Theory and Engineering 6, no. 2 (2014): 141–45. http://dx.doi.org/10.7763/ijcte.2014.v6.852.

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Andriani, Siska. "Distribution Analysis Active Small and Medium Industries Bogor City Using K-means Clustering." Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 20, no. 1 (December 2, 2022): 56–70. http://dx.doi.org/10.33751/komputasi.v20i1.6559.

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Small and Medium Industries (IKM) is one sector that contributes to driving economic growth, one of which is in West Java province, Bogor city. The number of active IKM in the city of Bogor in 2021 based on the survey results was 1,189, while in 2022 the number of small and medium industries (IKM) active in the city of Bogor based on the survey results was 1,766. The purpose of this study was conducted to determine the distribution of active small and medium industries (IKM) in Bogor city. So, this research can provide solutions related to the government or agencies to assist in building and developing IKM. In this research, the method used is the Knowledge Discovery in Database method, Where the stages are data selection, pre-processing, transformation, data mining and evaluation. Determination of the number of clusters is done using the elbow method. After determining with Elbow, the data will be represented using K-means clustering. The results of the K-means clustering algorithm yield 3 clusters, with each cluster 0 criterion being the distribution of low IKM with a total of 45 sub-districts. Cluster 1 is the distribution of medium IKM with the number of sub-districts is 14, and cluster 2 is the distribution of high IKM with the number of sub-districts totaling 9. The evaluation in this study used the silhouette coefficient method, from the data used it produced a cluster value of 0.56 which means that it is included in the clustering criteria with a good structure.

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Soleh, Muhamad, Aniati Murni Arymurthy, and Sesa Wiguna. "CHANGE DETECTION IN MULTI-TEMPORAL IMAGES USING MULTISTAGE CLUSTERING FOR DISASTER RECOVERY PLANNING." Jurnal Ilmu Komputer dan Informasi 11, no. 2 (June 29, 2018): 110. http://dx.doi.org/10.21609/jiki.v11i2.623.

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Change detection analysis on multi-temporal images using various methods have been developed by many researchers in the field of spatial data analysis and image processing. Change detection analysis has many benefit for real world applications such as medical image analysis, valuable material detector, satellite image analysis, disaster recovery planning, and many others. Indonesia is one of the most country that encounter natural disaster. The most memorable disaster was happened in December 26, 2004. Change detection is one of the important part management planning for natural disaster recovery. This article present the fast and accurate result of change detection on multi-temporal images using multistage clustering. There are three main step for change detection in this article, the first step is to find the image difference of two multi-temporal images between the time before disaster and after disaster using operation log ratio between those images. The second step is clustering the difference image using Fuzzy C means divided into three classes. Change, unchanged, and intermediate change region. Afterword the last step is cluster the change map from fuzzy C means clustering using k means clustering, divided into two classes. Change and unchanged region. Both clustering’s based on Euclidian distance.

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Zoghlami, Mohamed Ali, Minyar Sassi Hidri, and Rahma Ben Ayed. "Consensus-Driven Cluster Analysis: Top-Down and Bottom-Up Based Split-and-Merge Classifiers." International Journal on Artificial Intelligence Tools 26, no. 04 (August 2017): 1750018. http://dx.doi.org/10.1142/s021821301750018x.

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Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Typically, the goal is searching for the socalled median (or consensus) partition, i.e. the partition that is most similar, on average, to all the input partitions. In this paper we address the problem of combining multiple fuzzy clusterings without access to the underlying features of the data while basing on inter-clusters similarity. We are concerned of top-down and bottom-up based consensus-driven fuzzy clustering while splitting and merging worst clusters. The objective is to reconcile a structure, developed for patterns in some dataset with the structural findings already available for other related ones. The proposed classifiers consider dispersion and dissimilarity between the partitions as well as the corresponding fuzzy proximity matrices. Several illustrative numerical examples, using both synthetic data and those coming from available machine learning repositories, are also included. The experimental component of the study shows the efficiency of the proposed classifiers in terms of quality and runtime.

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Biswas, Ashis Kumer, and Jean X. Gao. "PR2S2Clust: Patched RNA-seq read segments’ structure-oriented clustering." Journal of Bioinformatics and Computational Biology 14, no. 05 (October 2016): 1650027. http://dx.doi.org/10.1142/s021972001650027x.

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RNA-seq, the next generation sequencing platform, enables researchers to explore deep into the transcriptome of organisms, such as identifying functional non-coding RNAs (ncRNAs), and quantify their expressions on tissues. The functions of ncRNAs are mostly related to their secondary structures. Thus by exploring the clustering in terms of structural profiles of the corresponding read-segments would be essential and this fuels in our motivation behind this research. In this manuscript we proposed PR2S2Clust, Patched RNA-seq Read Segments’ Structure-oriented Clustering, which is an analysis platform to extract features to prepare the secondary structure profiles of the RNA-seq read segments. It provides a strategy to employ the profiles to annotate the segments into ncRNA classes using several clustering strategies. The system considers seven pairwise structural distance metrics by considering short-read mappings onto each structure, which we term as the “patched structure” while clustering the segments. In this regard, we show applications of both classical and ensemble clusterings of the partitional and hierarchical variations. Extensive real-world experiments over three publicly available RNA-seq datasets and a comparative analysis over four competitive systems confirm the effectiveness and superiority of the proposed system. The source codes and dataset of PR2S2Clust are available at the http://biomecis.uta.edu/~ashis/res/PR2S2Clust-suppl/ .

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Hidayat, Syahroni, Ria Rismayati, Muhammad Tajuddin, and Ni Luh Putu Merawati. "Segmentation of university customers loyalty based on RFM analysis using fuzzy c-means clustering." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (March 11, 2020): 133–39. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.133-139.

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One of the strategic plans of the developing universities in obtaining new students is forming a partnership with surrounding high schools. However, partnerships made does not always behave as expected. This paper presented the segmentation technique to the previous new student admission dataset using the integration of recency, frequency, and monetary (RFM) analysis and fuzzy c-means (FCM) algorithm to evaluate the loyalty of the entire school that has bound the partnership with the institution. The dataset is converted using the RFM approach before processed with the FCM algorithm. The result reveals that the schools can be segmented, respectively, as high potential (SP), potential (P), low potential (CP), and very low potential (KP) categories with PCI value 0.86. From the analysis of SP, P, and CP, only 71 % of 52 school partners categorized as loyal partners.

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BAŞARAN, Bülent. "Examining Preservice Teachers’ TPACK-21 Efficacies with Clustering Analysis in Terms of Certain Variables." Malaysian Online Journal of Educational Technology 8, no. 3 (July 1, 2020): 84–99. http://dx.doi.org/10.17220/mojet.2020.03.005.

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Motozawa, Masaaki, Yuta Iizuka, and Tatsuo Sawada. "OS8-2-2 Ultrasonic analysis of clustering structures in magnetic fluid under magnetic field." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2007.6 (2007): _OS8–2–2–1—_OS8–2–2–5. http://dx.doi.org/10.1299/jsmeatem.2007.6._os8-2-2-1.

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Anthony, J. "Analysis: Mustering for clustering." Engineering & Technology 3, no. 18 (October 25, 2008): 61. http://dx.doi.org/10.1049/et:20081810.

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Serban, Nicoleta, and Huijing Jiang. "Multilevel Functional Clustering Analysis." Biometrics 68, no. 3 (February 7, 2012): 805–14. http://dx.doi.org/10.1111/j.1541-0420.2011.01714.x.

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Freudenberg, Johannes M., Vineet K. Joshi, Zhen Hu, and Mario Medvedovic. "CLEAN: CLustering Enrichment ANalysis." BMC Bioinformatics 10, no. 1 (2009): 234. http://dx.doi.org/10.1186/1471-2105-10-234.

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Bruno, Giulia, and Alessandro Fiori. "MicroClAn: Microarray clustering analysis." Journal of Parallel and Distributed Computing 73, no. 3 (March 2013): 360–70. http://dx.doi.org/10.1016/j.jpdc.2012.09.008.

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Guthke, R., and R. Ro�mann. "Fermentation analysis by clustering." Bioprocess Engineering 6, no. 4 (April 1991): 157–61. http://dx.doi.org/10.1007/bf00369253.

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Ackermann, Marcel R., Johannes Blömer, Daniel Kuntze, and Christian Sohler. "Analysis of Agglomerative Clustering." Algorithmica 69, no. 1 (December 12, 2012): 184–215. http://dx.doi.org/10.1007/s00453-012-9717-4.

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Koszalka, Inga Monika, and Joseph H. LaCasce. "Lagrangian analysis by clustering." Ocean Dynamics 60, no. 4 (June 9, 2010): 957–72. http://dx.doi.org/10.1007/s10236-010-0306-2.

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Tang, Shaoqiang, Lei Zhang, and Wing Kam Liu. "From virtual clustering analysis to self-consistent clustering analysis: a mathematical study." Computational Mechanics 62, no. 6 (March 24, 2018): 1443–60. http://dx.doi.org/10.1007/s00466-018-1573-x.

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Swanson, David M., Tonje Lien, Helga Bergholtz, Therese Sørlie, and Arnoldo Frigessi. "A Bayesian two-way latent structure model for genomic data integration reveals few pan-genomic cluster subtypes in a breast cancer cohort." Bioinformatics 35, no. 23 (May 11, 2019): 4886–97. http://dx.doi.org/10.1093/bioinformatics/btz381.

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Abstract Motivation Unsupervised clustering is important in disease subtyping, among having other genomic applications. As genomic data has become more multifaceted, how to cluster across data sources for more precise subtyping is an ever more important area of research. Many of the methods proposed so far, including iCluster and Cluster of Cluster Assignments (COCAs), make an unreasonable assumption of a common clustering across all data sources, and those that do not are fewer and tend to be computationally intensive. Results We propose a Bayesian parametric model for integrative, unsupervised clustering across data sources. In our two-way latent structure model, samples are clustered in relation to each specific data source, distinguishing it from methods like COCAs and iCluster, but cluster labels have across-dataset meaning, allowing cluster information to be shared between data sources. A common scaling across data sources is not required, and inference is obtained by a Gibbs Sampler, which we improve with a warm start strategy and modified density functions to robustify and speed convergence. Posterior interpretation allows for inference on common clusterings occurring among subsets of data sources. An interesting statistical formulation of the model results in sampling from closed-form posteriors despite incorporation of a complex latent structure. We fit the model with Gaussian and more general densities, which influences the degree of across-dataset cluster label sharing. Uniquely among integrative clustering models, our formulation makes no nestedness assumptions of samples across data sources so that a sample missing data from one genomic source can be clustered according to its existing data sources. We apply our model to a Norwegian breast cancer cohort of ductal carcinoma in situ and invasive tumors, comprised of somatic copy-number alteration, methylation and expression datasets. We find enrichment in the Her2 subtype and ductal carcinoma among those observations exhibiting greater cluster correspondence across expression and CNA data. In general, there are few pan-genomic clusterings, suggesting that models assuming a common clustering across genomic data sources might yield misleading results. Availability and implementation The model is implemented in an R package called twl (‘two-way latent’), available on CRAN. Data for analysis are available within the R package. Supplementary information Supplementary data are available at Bioinformatics online.

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Sandhya Rani, Yalamarthi Leela, V. Sucharita, and K. V. V. Satyanarayana. "Extensive Analysis on Generation and Consensus Mechanisms of Clustering Ensemble: A Survey." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 4 (August 1, 2018): 2351. http://dx.doi.org/10.11591/ijece.v8i4.pp2351-2357.

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<p class="PreformattedText">Data analysis plays a prominent role in interpreting various phenomena. Data mining is the process to hypothesize useful knowledge from the extensive data. Based upon the classical statistical prototypes the data can be exploited beyond the storage and management of the data. Cluster analysis a primary investigation with little or no prior knowledge, consists of research and development across a wide variety of communities. Cluster ensembles are melange of individual solutions obtained from different clusterings to produce final quality clustering which is required in wider applications. The method arises in the perspective of increasing robustness, scalability and accuracy. This paper gives a brief overview of the generation methods and consensus functions included in cluster ensemble. The survey is to analyze the various techniques and cluster ensemble methods.</p>

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Chu, Yichen, and Xiaojian Yin. "Data Analysis of College Students’ Mental Health Based on Clustering Analysis Algorithm." Complexity 2021 (April 12, 2021): 1–10. http://dx.doi.org/10.1155/2021/9996146.

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Mental health is an important basic condition for college students to become adults. Educators gradually attach importance to strengthening the mental health education of college students. This paper makes a detailed analysis and research on college students’ mental health, expounds the development and application of clustering analysis algorithm, applies the distance formula and clustering criterion function commonly used in clustering analysis, and makes a specific description of some classic algorithms of clustering analysis. Based on expounding the advantages and disadvantages of fast-clustering analysis algorithm and hierarchical clustering analysis algorithm, this paper introduces the concept of the two-step clustering algorithm, discusses the algorithm flow of clustering model in detail, and gives the algorithm flow chart. The main work of this paper is to analyze the clustering algorithm of students’ mental health database formed by mental health assessment tool test, establish a data mining model, mine the database, analyze the state characteristics of different college students’ mental health, and provide corresponding solutions. In order to meet the needs of the psychological management system based on the clustering analysis method, the clustering analysis algorithm is used to cluster the data. Based on the original database, this paper establishes the methods of selecting, cleaning, and transforming the data of students’ psychological archives. Finally, it expounds on the application of data mining in students’ psychological management system and summarizes and prospects the implementation of the system.

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Ronald, Ronald, and Amelia Amelia. "Clustering Analysis of Students’ Culture and Behavior for University Choice Using Kohonen Self Organizing Map." Indian Journal of Applied Research 4, no. 3 (October 1, 2011): 270–73. http://dx.doi.org/10.15373/2249555x/mar2014/83.

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K, Sivakumar. "Statistical Analysis on the Impact of Clustering Techniques on the Improvement of Supervised Learning Algorithms." Journal of Advanced Research in Dynamical and Control Systems 11, no. 11-SPECIAL ISSUE (February 20, 2019): 388–94. http://dx.doi.org/10.5373/jardcs/v11sp11/20193046.

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Santos, Victor Malagon, Ivan D. Haigh, and Thomas Wahl. "SPATIAL AND TEMPORAL CLUSTERING ANALYSIS OF EXTREME WAVE EVENTS AROUND THE UK COASTLINE." Coastal Engineering Proceedings, no. 36 (December 30, 2018): 76. http://dx.doi.org/10.9753/icce.v36.waves.76.

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In northern Europe and the UK in particular, a remarkable series of storms occurred over the winter of 2013/14, with large waves which led to considerable damage to coastal infrastructure. The most significant features of this storm season were the length of coastline affected by flooding (i.e., â€˜spatial footprintsâ€™) and the short inter-arrival times between extreme events (i.e., â€˜temporal clusteringâ€™) (Haigh et al., 2016). These extreme wave event characteristics had a large contribution to the devastating consequences along the coast, yet little attention has been paid to them in previous studies. The main aim of this study is to assess the spatial footprints and the temporal clustering of extreme wave events around the UK to facilitate the inclusion of such information into coastal management.

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Digel, Ivan, Dinara Mussabalina, Marat Urdabayev, Nurbakhyt Nurmukhametov, and Aigul Akparova. "Evaluating development prospects of smart cities: Cluster analysis of Kazakhstan’s regions." Problems and Perspectives in Management 20, no. 4 (October 20, 2022): 76–87. http://dx.doi.org/10.21511/ppm.20(4).2022.07.

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This study aims to study Kazakhstan’s regions and identify places with the best potential for developing smart cities based on cluster analysis. To analyze the differentiation by the level of development, 17 regions of Kazakhstan are grouped according to 2020 data from the statistical bulletin of the National Bureau of Statistics of the Republic of Kazakhstan. The formation of groups of regions with different values of indicators was carried out based on agglomerative clustering using the single linkage, complete linkage, and Ward’s clustering methods. In agglomerative clustering, the algorithm groups regions based on observations into clusters, and indicators determine each area’s innovative development level. The instrument to build clustering is the “RStudio” software package. As a result, regions with their essential characteristics were identified, and an assessment of their prospects was obtained with the most significant potential for developing and managing “smart cities” – Atyrau region, Almaty city, and Astana city. The remaining clusters include regions where favorable conditions for the development of innovations have not yet been formed, which require more resources and efforts to build “smart cities.” Therefore, they should not be the first to implement this concept. They need a more balanced, integrated approach, ideally supported by experience in implementing the idea in more promising regions. In a sense, clustering also allowed for identifying potential (or even existing) innovation clusters in regions of Kazakhstan. The study results can be used in developing government programs to form smart cities and further study the potential of smart cities.

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