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Journal articles on the topic 'Divisive hierarchical clustering'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Anagnostou, Panagiotis, Sotiris Tasoulis, Vassilis P. Plagianakos, and Dimitris Tasoulis. "HiPart: Hierarchical Divisive Clustering Toolbox." Journal of Open Source Software 8, no. 84 (2023): 5024. http://dx.doi.org/10.21105/joss.05024.

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Simion, Ileana M., Augustin-C. Moţ, and Costel Sârbu. "Finding specific peaks (markers) using fuzzy divisive hierarchical associative-clustering based on the chromatographic profiles of medicinal plant extracts obtained at various detection wavelengths." Analytical Methods 12, no. 25 (2020): 3260–67. http://dx.doi.org/10.1039/d0ay00295j.

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Advanced chemometric methods, such as fuzzy c-means (FCM), a fuzzy divisive hierarchical clustering algorithm (FDHC), and fuzzy divisive hierarchical associative-clustering (FDHAC), have been successfully applied in this study.

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Nietto, Paulo Rogério, and Maria Do Carmo Nicoletti. "Case studies in divisive hierarchical clustering." International Journal of Innovative Computing and Applications 8, no. 2 (2017): 102. http://dx.doi.org/10.1504/ijica.2017.084893.

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Nietto, Paulo Rogério, and Maria Do Carmo Nicoletti. "Case studies in divisive hierarchical clustering." International Journal of Innovative Computing and Applications 8, no. 2 (2017): 102. http://dx.doi.org/10.1504/ijica.2017.10005945.

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5

Xiong, Tengke, Shengrui Wang, André Mayers, and Ernest Monga. "DHCC: Divisive hierarchical clustering of categorical data." Data Mining and Knowledge Discovery 24, no. 1 (2011): 103–35. http://dx.doi.org/10.1007/s10618-011-0221-2.

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6

Harahap, Ahir Yugo Nugroho, Ria Eka Sari, Heri Gunawan, and Adnan Buyung Nasution. "Evaluation of Product Sales Data Using Clustering Method and Hierarchical Divisive Clustering at PT.AYN." Indonesian Journal of Interdisciplinary Research in Science and Technology 2, no. 7 (2024): 1145–58. http://dx.doi.org/10.55927/marcopolo.v2i7.10442.

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Data Mining, focusing on the Hierarchical Divisive algorithm, can provide a solution for PT.AYN in overcoming the problem of unifying and evaluating sales data, a company that sells various types of disposable tissues. This study aims to identify products that are in demand and less in demand and to group sales data based on company and product type. The results of this study provide valuable insights for evaluating sales data, understanding distributor purchasing trends, and supporting more effective stock planning, shipping, and marketing strategies. Through the application of the clustering

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Chen, Lin Chih. "Building a Post-Search Academic Search Engine Based on a Serial of Clustering Methods." Applied Mechanics and Materials 284-287 (January 2013): 3051–55. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.3051.

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Academic search engines, such as Google Scholar and Scirus, provide a Web-based interface to effectively find relevant scientific articles to researchers. However, current academic search engines are lacking the ability to cluster the search results into a hierarchical tree structure. In this paper, we develop a post-search academic search engine by using a mixed clustering method. In this method, we first adopt a suffix tree clustering and a two-way hash mechanism to generate all meaningful labels. We then develop a divisive hierarchical clustering algorithm to organize the labels into a hier

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Kamat, Prof Vaishnavi, Prof Terence Johnson, Rudresh Chodankar, Rama Harmalkar, Gauresh Naik, and Prajyot Narulkar. "Document Clustering Using Divisive Hierarchical Bisecting Min Max Clustering Algorithm." IOSR Journal of Computer Engineering 19, no. 03 (2017): 66–70. http://dx.doi.org/10.9790/0661-1903066670.

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Chavent, Marie, Yves Lechevallier, and Olivier Briant. "DIVCLUS-T: A monothetic divisive hierarchical clustering method." Computational Statistics & Data Analysis 52, no. 2 (2007): 687–701. http://dx.doi.org/10.1016/j.csda.2007.03.013.

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Luo, Junhai, Lei Ye, and Xiaoting He. "Neighbors-based divisive algorithm for hierarchical analysis in networks." International Journal of Modern Physics C 30, no. 07 (2019): 1940003. http://dx.doi.org/10.1142/s0129183119400035.

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Hierarchical analysis for network structure can point out which communities can constitute a larger group or give reasonable smaller groups within a community. Numerous methods for discovering community in networks divide networks at only one certain granularity, which does not benefit hierarchical analysis for network structure. Hierarchical clustering algorithms are the common technique that reveals the multilevel structure in the network analysis. In this work, we give a definition for scores of edges according to the basic idea of means clustering. Based on the definition, a neighbors-base

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Marengo, E., and R. Todeschini. "Linear discriminant hierarchical clustering: A modeling and cross-validable divisive clustering method." Chemometrics and Intelligent Laboratory Systems 19, no. 1 (1993): 43–51. http://dx.doi.org/10.1016/0169-7439(93)80081-r.

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Li, Shaoning, Wenjing Li, and Jia Qiu. "A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis." ISPRS International Journal of Geo-Information 6, no. 1 (2017): 30. http://dx.doi.org/10.3390/ijgi6010030.

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13

Xia, Qingjun, Xueming Li, Ye Song, and Baocheng Zhang. "Aircraft grouping based on improved divisive hierarchical clustering algorithm." Journal of Air Transport Management 40 (August 2014): 157–62. http://dx.doi.org/10.1016/j.jairtraman.2014.07.002.

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14

LEE, CHOON SHIK, and HARK HWANG. "A HIERARCHICAL DIVISIVE CLUSTERING METHOD FOR MACHINE-COMPONENT GROUPING PROBLEMS." Engineering Optimization 17, no. 1-2 (1991): 65–78. http://dx.doi.org/10.1080/03052159108941061.

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15

Roux, Maurice. "A Comparative Study of Divisive and Agglomerative Hierarchical Clustering Algorithms." Journal of Classification 35, no. 2 (2018): 345–66. http://dx.doi.org/10.1007/s00357-018-9259-9.

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16

Izadpanah, Najva. "A Divisive Hierarchical Clustering Based Method for Indexing Image Information." Signal & Image Processing : An International Journal 6, no. 1 (2015): 13–32. http://dx.doi.org/10.5121/sipij.2015.6102.

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17

Guénoche, A., P. Hansen, and B. Jaumard. "Efficient algorithms for divisive hierarchical clustering with the diameter criterion." Journal of Classification 8, no. 1 (1991): 5–30. http://dx.doi.org/10.1007/bf02616245.

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18

Sitikantha, Sitikantha, Suneeta Mohanty, and Bhabani Shankar Prasad Mishra. "Neutrosophic Hierarchical Clustering: A Novel Approach for Handling Uncertainty in Multi-Level Data Organization." International Journal of Neutrosophic Science 26, no. 1 (2025): 243–53. https://doi.org/10.54216/ijns.260121.

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The most important stage of data mining is clustering. Several distinct clustering approaches like grid-based, density-based, partitioning, graph-based, model-based, and hierarchical clustering are used for cluster analysis. We can cluster data objects into hierarchical trees by using the hierarchical clustering approach. Hierarchical clustering, with its agglomerative and divisive types, uses nodes to represent clusters. Agglomerative clustering is favored, and high-quality clusters are essential for successful cluster analysis. Up to this point, numerous alternatives to the clustering techni

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19

Mogaraju, Jagadish Kumar. "Agglomerative and Divisive hierarchical cluster analysis of groundwater quality variables using opensource tools over YSR district, AP, India." Journal of Scientific Research 66, no. 04 (2022): 15–20. http://dx.doi.org/10.37398/jsr.2022.660403.

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Groundwater quality variables like F, Total Hardness (TH), Total Alkalinity (TA), Total Dissolved Solids (TDS), SO4, SAR, NA, EC, Cl, Ca, Mg, and pH were tested with Hierarchical clustering analysis (HCA) to identify the groupings or clusters that exist in the dataset. The dataset is subjected to Agglomerative and divisive hierarchical clustering. The observations were scaled to compare variables systematically. The clustering structure was determined using an agglomerative coefficient. Agglomerative approaches like complete, average, single, and ward are tested using agglomerative coefficient

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S. Sivasankari. "Fuzzy Membership Partition Based Effective Hierarchical Agglomerative Flat Clustering Method for High Dimensional Data." Communications on Applied Nonlinear Analysis 31, no. 6s (2024): 538–53. http://dx.doi.org/10.52783/cana.v31.1242.

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Introduction: Hierarchical clustering is an unsupervised powerful method for empirical knowledge interpretation from data. It has a fundamental role in understanding the complex pattern in huge datasets. It creates a hierarchical representation of data by forming clusters in two ways namely Agglomerative (Bottom-up) and Divisive (Top-Down). The main advantage is that it does not need to fix number of clusters. Objectives: To handle the issues such as, the pertinence for enormous data is minimal as the computational complexity is high in using Hierarchical clustering, complication of fixing Thr

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Peter, S. John, and S. Chidambaranathan. "An efficient divisive-agglomerative hierarchical clustering algorithm using minimum spanning tree." Journal of Discrete Mathematical Sciences and Cryptography 14, no. 6 (2011): 583–95. http://dx.doi.org/10.1080/09720529.2011.10698357.

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22

Bordogna, Gloria, and Gabriella Pasi. "A quality driven Hierarchical Data Divisive Soft Clustering for information retrieval." Knowledge-Based Systems 26 (February 2012): 9–19. http://dx.doi.org/10.1016/j.knosys.2011.06.012.

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23

Sârbu, Costel, Katharina Zehl, and Jürgen W. Einax. "Fuzzy divisive hierarchical clustering of soil data using Gustafson–Kessel algorithm." Chemometrics and Intelligent Laboratory Systems 86, no. 1 (2007): 121–29. http://dx.doi.org/10.1016/j.chemolab.2006.08.015.

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24

Wang, Yuyan, and Benjamin Moseley. "An Objective for Hierarchical Clustering in Euclidean Space and Its Connection to Bisecting K-means." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6307–14. http://dx.doi.org/10.1609/aaai.v34i04.6099.

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This paper explores hierarchical clustering in the case where pairs of points have dissimilarity scores (e.g. distances) as a part of the input. The recently introduced objective for points with dissimilarity scores results in every tree being a ½ approximation if the distances form a metric. This shows the objective does not make a significant distinction between a good and poor hierarchical clustering in metric spaces.Motivated by this, the paper develops a new global objective for hierarchical clustering in Euclidean space. The objective captures the criterion that has motivated the use of

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25

Hamzah Abdul Hamid, Yap Bee Wah, Khatijahhusna Abdul Rani, and Xian Jin Xie. "The Effect Of Divisive Analysis Clustering Technique On Goodness-Of-Fit Test For Multinomial Logistic Regression." Journal of Advanced Research in Applied Sciences and Engineering Technology 48, no. 2 (2024): 39–48. http://dx.doi.org/10.37934/araset.48.2.3948.

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The relationship between a categorical dependent variable and independent variable(s) are usually modelled using the logistic regression method. There are three types of logistic regression: binary, multinomial, and ordinal. When there is two cayegories of dependent variable, binary logistic regression is used while when there is more than two nominal categories of dependent variable, multinomial logistic regression is employed. Ordinal logistic regression is used when the dependent variable contains more than two ordinal categories. All regression models should be checked after being fitted t

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Qin, Hongwu, Xiuqin Ma, Tutut Herawan, and Jasni Mohamad Zain. "MGR: An information theory based hierarchical divisive clustering algorithm for categorical data." Knowledge-Based Systems 67 (September 2014): 401–11. http://dx.doi.org/10.1016/j.knosys.2014.03.013.

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27

Celebi, M. Emre, Quan Wen, and Sae Hwang. "An effective real-time color quantization method based on divisive hierarchical clustering." Journal of Real-Time Image Processing 10, no. 2 (2012): 329–44. http://dx.doi.org/10.1007/s11554-012-0291-4.

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28

Nedyalkova, Miroslava, Costel Sarbu, Marek Tobiszewski, and Vasil Simeonov. "Fuzzy Divisive Hierarchical Clustering of Solvents According to Their Experimentally and Theoretically Predicted Descriptors." Symmetry 12, no. 11 (2020): 1763. http://dx.doi.org/10.3390/sym12111763.

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The present study describes a simple procedure to separate into patterns of similarity a large group of solvents, 259 in total, presented by 15 specific descriptors (experimentally found and theoretically predicted physicochemical parameters). Solvent data is usually characterized by its high variability, different molecular symmetry, and spatial orientation. Methods of chemometrics can usefully be used to extract and explore accurately the information contained in such data. In this order, advanced fuzzy divisive hierarchical-clustering methods were efficiently applied in the present study of

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29

Santoso, Agus, and Siti Nurhayati. "ALGORITHMIC GUARANTEES FOR HIERARCHICAL DATA GROUPING: INSIGHTS FROM AVERAGE LINKAGE, BISECTING K-MEANS, AND LOCAL SEARCH HEURISTICS." International Journal of Intelligent Data and Machine Learning 2, no. 02 (2025): 8–13. https://doi.org/10.55640/ijidml-v02i02-02.

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Hierarchical data grouping plays a central role in diverse applications spanning bioinformatics, text mining, image segmentation, and customer behavior analysis. While a multitude of clustering algorithms have been proposed, including agglomerative techniques, divisive strategies, and heuristic optimizations, understanding their algorithmic guarantees and comparative performance remains an ongoing research challenge. This study provides a rigorous examination of the theoretical and empirical properties of three prominent approaches: average linkage clustering, bisecting k-means, and local sear

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30

Zhong, Caiming, Duoqian Miao, Ruizhi Wang, and Xinmin Zhou. "DIVFRP: An automatic divisive hierarchical clustering method based on the furthest reference points." Pattern Recognition Letters 29, no. 16 (2008): 2067–77. http://dx.doi.org/10.1016/j.patrec.2008.07.002.

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31

Kumarahadi, Brigitta Melati, Hasih Pratiwi, and Sri Subanti. "Penerapan Metode Hierarchical Clustering Untuk Pengelompokan Kota/Kabupaten di Indonesia Berdasarkan Indikator Kemiskinan." Jurnal Teknologi Informasi dan Komunikasi (TIKomSiN) 11, no. 2 (2023): 13. https://doi.org/10.30646/tikomsin.v11i2.754.

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In 2024, the government sets a poverty target in Indonesia of 6-7%. Until September 2022, poverty still shows a figure of 9.57%. To achieve the target, it is necessary to determine priority areas so that government policies can be right on target. This study aims to group cities/regencies in Indonesia based on poverty indicators as a solution to obtain priority areas using the clustering method. This method is used to collect data into several groups based on the same criteria. Hierarchical clustering consists of several methods, including agglomerative nesting such as single linkage, complete

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32

Wijuniamurti, Susi, Sigit Nugroho, and Ramya Rachmawati. "Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method For Hierarchical Clustering On Some Distance Measurement Concepts." Journal of Statistics and Data Science 1, no. 1 (2022): 7–11. http://dx.doi.org/10.33369/jsds.v1i1.21009.

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Clustering data through hierarchical approach could be performed by Agglomerative Nesting (AGNES) Method and Divisive Analysis (DIANA) Method. The objective of this research is to compare both the methods based on Euclid and Manhattan distance measurements. Of this research the clustering procedures of agglomerative method are conducted by exploring all techniques including single linkage, complete linkage, average linkage, and Ward. The data used are the National Socio-Economic Survey (SUSENAS) data which are selected specifically for the percentage of over 5 year old residents in each provin

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Mărghitaş, Liviu Al, Daniel S. Dezmirean, and Otilia Bobiş. "Important Developments in Romanian Propolis Research." Evidence-Based Complementary and Alternative Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/159392.

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The most important developments in propolis analysis and pharmacological properties are discussed. In order to help in the Romanian propolis standardization, different methodologies for chemical composition analysis (UV-VIS, HP-TLC, and HPLC-DAD) are reviewed using new approaches and software (fuzzy divisive hierarchical clustering approach and ChromQuest software) and compared with international studies made until now in propolis research. Practical applications of Romanian propolis in medicinal therapy and cosmetics are reviewed, and quality criteria for further standardization are proposed.

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Hurbánková, Ľubica, and Dominika Krasňanská. "STATISTICAL ANALYSIS OF THE EUROPEAN UNION COUNTRIES ON THE BASIS OF SELECTED SOCIO-ECONOMIC AND DEMOGRAPHIC INDICATORS." Balkans Journal of Emerging Trends in Social Sciences 2, no. 1 (2019): 88–96. http://dx.doi.org/10.31410/balkans.jetss.2019.2.1.88-96.

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The aim of the paper is to compare the European Union countries on the basis of selected socio-economic and demographic indicators for the year 2016. The following indicators are selected for analysis: gross domestic product per capita, government gross debt as a percentage of gross domestic product, inflation rate, unemployment rate, total fertility rate, infant mortality rate and crude divorce rate. The contribution of the paper is a division of the countries of the European Union into several groups using cluster analysis so that the countries belonging to the same cluster are as similar as

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Mautz, Dominik, Claudia Plant, and Christian Böhm. "DeepECT: The Deep Embedded Cluster Tree." Data Science and Engineering 5, no. 4 (2020): 419–32. http://dx.doi.org/10.1007/s41019-020-00134-0.

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Abstract The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been shown to be advantageous over separately optimizing them. So far, however, all proposed methods have been using a flat clustering strategy, with the actual number of clusters known a priori. In this paper, we propose the Deep Embedded Cluster Tree (DeepECT), the first divisive hierarchical embedded clustering method. The cluster tree does not need to

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Masuyama, Naoki, Narito Amako, Yuna Yamada, Yusuke Nojima, and Hisao Ishibuchi. "Adaptive Resonance Theory-Based Topological Clustering With a Divisive Hierarchical Structure Capable of Continual Learning." IEEE Access 10 (2022): 68042–56. http://dx.doi.org/10.1109/access.2022.3186479.

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37

Dumitrescu, Dan, Costel Sǎrbu, and Horia Pop. "A Fuzzy Divisive Hierarchical Clustering Algorithm for the Optimal Choice of Sets of Solvent Systems." Analytical Letters 27, no. 5 (1994): 1031–54. http://dx.doi.org/10.1080/00032719408007370.

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38

Mustamiin, Muhamad, Ahmad Lubis Ghozali, and Muhammad Lukman Sifa. "Peringkasan Multi-dokumen menggunakan Metode Pengelompokkan berbasis Hirarki dengan Multi-level Divisive Coefficient." Jurnal Teknologi Informasi dan Ilmu Komputer 5, no. 6 (2018): 697. http://dx.doi.org/10.25126/jtiik.2018561149.

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<p class="Abstrak">Peringkasan merupakan salah satu bagian dari perolehan informasi yang bertujuan untuk mendapatkan informasi secara cepat dan efisien dengan membuat intisari dari suatu dokumen. Dokumen-dokumen khususnya dokumen laporan setiap hari semakin bertambah seiring dengan bertambahnya pelaksanaan suatu kegiatan atau acara. Kebutuhan informasi yang semakin cepat, jumlah dokumen yang semakin bertambah banyak membuat kebutuhan akan adanya peringkasan dokumen semakin tinggi. Peringkasan yang digunakan untuk meringkas lebih dari satu dokumen disebut peringkasan multi-dokumen. Untuk

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Huang, Kai-Yi. "The use of a newly developed algorithm of divisive hierarchical clustering for remote sensing image analysis." International Journal of Remote Sensing 23, no. 16 (2002): 3149–68. http://dx.doi.org/10.1080/01431160110070807.

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Ramos, M. C. "Divisive and hierarchical clustering techniques to analyse variability of rainfall distribution patterns in a Mediterranean region." Atmospheric Research 57, no. 2 (2001): 123–38. http://dx.doi.org/10.1016/s0169-8095(01)00065-5.

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41

Vasyltcova, Nataliia V., and Iryna Yu Panforova. "Research on the use of hierarchical clustering methods when solving the task of IT product configuration analysis." Management Information System and Devises, no. 178 (December 23, 2022): 37–49. http://dx.doi.org/10.30837/0135-1710.2022.178.037.

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The main features of existing methods for solving the problem of IT product configuration analyzing are considered. The main disadvantages of these methods are highlighted. It is proposed to divide the task of IT product configuration analyzing into two subtasks. Solutions to the subtask of forming a set of options for decomposing the description of the system architecture into separate functional configuration elements using divisive and agglomerative algorithms are considered. A comparative analysis of the application features of hierarchical clustering algorithms for solving this subtask is

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Grassi, Kelly, Émilie Poisson-Caillault, André Bigand, and Alain Lefebvre. "Comparative Study of Clustering Approaches Applied to Spatial or Temporal Pattern Discovery." Journal of Marine Science and Engineering 8, no. 9 (2020): 713. http://dx.doi.org/10.3390/jmse8090713.

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In the framework of ecological or environmental assessments and management, detection, characterization and forecasting of the dynamics of environmental states are of paramount importance. These states should reflect general patterns of change, recurrent or occasional events, long-lasting or short or extreme events which contribute to explain the structure and the function of the ecosystem. To identify such states, many scientific consortiums promote the implementation of Integrated Observing Systems which generate increasing amount of complex multivariate/multisource/multiscale datasets. Extr

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Rueda, Alice, and Sridhar Krishnan. "Clustering Parkinson’s and Age-Related Voice Impairment Signal Features for Unsupervised Learning." Advances in Data Science and Adaptive Analysis 10, no. 02 (2018): 1840007. http://dx.doi.org/10.1142/s2424922x18400077.

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This study focuses on the possibility of remote monitoring and screening of Parkinson’s and age-related voice impairment for the general public using self-recorded data on readily available or emerging technologies such as Smartphone and IoT devices. While most studies use professionally recorded voice in a controlled environment, this study uses self-recorded sustained vowel /a/ recordings using iPhone. Each healthy control (HC) and people with Parkinson’s (PWP) group has 57 age-matching mixed-gender subjects. The control subjects can have age-related voice impairment. Without severity labels

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Patnaik, Ashish Kumar, Prasanta Kumar Bhuyan, and K. V. Krishna Rao. "Divisive Analysis (DIANA) of hierarchical clustering and GPS data for level of service criteria of urban streets." Alexandria Engineering Journal 55, no. 1 (2016): 407–18. http://dx.doi.org/10.1016/j.aej.2015.11.003.

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Selvi, Huseyin Zahit, and Burak Caglar. "Using cluster analysis methods for multivariate mapping of traffic accidents." Open Geosciences 10, no. 1 (2018): 772–81. http://dx.doi.org/10.1515/geo-2018-0060.

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Abstract Many factors affect the occurrence of traffic accidents. The classification and mapping of the different attributes of the resulting accident are important for the prevention of accidents. Multivariate mapping is the visual exploration of multiple attributes using a map or data reduction technique. More than one attribute can be visually explored and symbolized using numerous statistical classification systems or data reduction techniques. In this sense, clustering analysis methods can be used for multivariate mapping. This study aims to compare the multivariate maps produced by the K

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Rahman, Rizwan Ur, and Deepak Singh Tomar. "Web Bot Detection System Based on Divisive Clustering and K-Nearest Neighbor Using Biostatistics Features Set." International Journal of Digital Crime and Forensics 13, no. 6 (2021): 1–27. http://dx.doi.org/10.4018/ijdcf.20211101.oa6.

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Web bots are destructive programs that automatically fill the web form and steal the data from web sites. According to numerous web bot traffic reports, web bots traffic comprises of more than fifty percent of the total web traffic. An effective guard against the stealing of the data from web sites and automated web form is to identify and confirm the human user presence on web sites. In this paper, an efficient k-Nearest Neighbor algorithm using hierarchical clustering for web bot detection is proposed. Proposed technique exploits a novel taxonomy of web bot features known as Biostatistics Fe

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Feher, Ioana, Dana Alina Magdas, Cezara Voica, Gabriela Cristea, and Costel Sârbu. "Fuzzy Divisive Hierarchical Associative-Clustering Applied to Different Varieties of White Wines According to Their Multi-Elemental Profiles." Molecules 25, no. 21 (2020): 4955. http://dx.doi.org/10.3390/molecules25214955.

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Wine data are usually characterized by high variability, in terms of compounds and concentration ranges. Chemometric methods can be efficiently used to extract and exploit the meaningful information contained in such data. Therefore, the fuzzy divisive hierarchical associative-clustering (FDHAC) method was efficiently applied in this study, for the classification of several varieties of Romanian white wines, using the elemental profile (concentrations of 30 elements analyzed by ICP-MS). The investigated wines were produced in four different geographical areas of Romania (Transylvania, Moldova,

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Mandilaras, Victoria, Swati Garg, Michael Cabanero, et al. "TP53 mutations in high grade serous ovarian cancer and impact on clinical outcomes: a comparison of next generation sequencing and bioinformatics analyses." International Journal of Gynecologic Cancer 29, no. 2 (2019): 346–52. http://dx.doi.org/10.1136/ijgc-2018-000087.

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ObjectiveMutations in TP53 are found in the majority of high grade serous ovarian cancers, leading to gain of function or loss of function of its protein product, p53, involved in oncogenesis. There have been conflicting reports as to the impact of the type of these on prognosis. We aim to further elucidate this relationship in our cohort of patients.Methods229 patients with high grade serous ovarian cancer underwent tumor profiling through an institutional molecular screening program with targeted next generation sequencing. TP53 mutations were classified using methods previously described in

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Kasoqi, Ilham Adnan, Memi Nor Hayati, and Rito Goejantoro. "PENGELOMPOKAN DESA ATAU KELURAHAN DI KUTAI KARTANEGARA MENGGUNAKAN ALGORITMA DIVISIVE ANALYSIS." Jurnal Statistika Universitas Muhammadiyah Semarang 9, no. 2 (2021): 101. http://dx.doi.org/10.26714/jsunimus.9.2.2021.101-108.

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Potential Villages (PODES) provide data on the existence, availability and development of the potential of each government administrative area. In order to make it easier for governments to make policies for a region, it is necessary to group the village and sub-districts. Cluster analysis is an analysis that aims to group objects based on the information that found in the data. One of the cluster analysis methods is the divisive analysis, which is a hierarchical grouping method with a top-down approach, where all objects are placed in one cluster and then sequentially divided into separate gr

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Cimiano, P., A. Hotho, and S. Staab. "Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis." Journal of Artificial Intelligence Research 24 (August 1, 2005): 305–39. http://dx.doi.org/10.1613/jair.1648.

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We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris' distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of

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