Relevant bibliographies by topics / Cluster analysis – Data processing

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Cluster analysis – Data processing'

Author: Grafiati
Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Cluster analysis – Data processing"

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Zanev, Vladimir, Stanislav Topalov, and Veselin Christov. "Analysis and Data Mining of Lead-Zinc Ore Data." Serdica Journal of Computing 7, no. 3 (2014): 271–80. http://dx.doi.org/10.55630/sjc.2013.7.271-280.

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This paper presents the results of our data mining study of Pb-Zn (lead-zinc) ore assay records from a mine enterprise in Bulgaria. We examined the dataset, cleaned outliers, visualized the data, and created dataset statistics. A Pb-Zn cluster data mining model was created for segmentation and prediction of Pb-Zn ore assay data. The Pb-Zn cluster data model consists of five clusters and DMX queries. We analyzed the Pb-Zn cluster content, size, structure, and characteristics. The set of the DMX queries allows for browsing and managing the clusters, as well as predicting ore assay records. A testing and validation of the Pb-Zn cluster data mining model was developed in order to show its reasonable accuracy before beingused in a production environment. The Pb-Zn cluster data mining model can be used for changes of the mine grinding and floatation processing parameters in almost real-time, which is important for the efficiency of the Pb-Zn ore beneficiation process.ACM Computing Classification System (1998): H.2.8, H.3.3.
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Karlashevych, Ivan, and Volodymyr Pravda. "Use of Cluster Analysis Method to Increase the Efficiency and Accuracy of Radar Data Processing." Computational Problems of Electrical Engineering 7, no. 1 (2017): 33–36. http://dx.doi.org/10.23939/jcpee2017.01.033.

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Ocampo, Daniel Morin, and Luiz Caldeira Brant de Tolentino-Neto. "Cluster Analysis for Data Processing in Educational Research." Acta Scientiae 21, no. 4 (2019): 34–48. http://dx.doi.org/10.17648/acta.scientiae.v21iss4id5119.

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Quantitative approaches to educational research have been undervalued and consequently less widely used. In this sense, this paper aims to present and analyze the techniques of Cluster Analysis as a possibility for research in sciences area. Therefore, the main hierarchical and non-hierarchical techniques of Cluster Analysis are presented, as well as some of their applications in educational research found in the literature. Cluster Analysis is adequate to simplify or elaborate hypotheses on massive data, such as large-scale educational research. The studies in the area of education that used Cluster Analysis methods proved to be fruitful to elicit results that collaborate with the area.
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Tkachev, Ivan, Roman Vasilyev, and Elena Belousova. "Cluster analysis of lightning discharges: based on Vereya-MR network data." Solar-Terrestrial Physics 7, no. 4 (2021): 85–92. http://dx.doi.org/10.12737/stp-74202109.

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Monitoring thunderstorm activity can help you solve many problems such as infrastructure facility protection, warning of hazardous phenomena associated with intense precipitation, study of conditions for the occurrence of thunderstorms and the degree of their influence on human activity, as well as the influence of thunderstorm activity on the formation of near-Earth space. We investigate the characteristics of thunderstorm cells by the method of cluster analysis. We take the Vereya-MR network data accumulated over a period from 2012 to 2018 as a basis. The Vereya-MR network considered in this paper is included in networks operating in the VLF-LF range (long and super-long radio waves). Reception points equipped with recording equipment, primary information processing systems, communication systems, precision time and positioning devices based on global satellite navigation systems are located throughout Russia. In the longitudinal-latitudinal thunderstorm distributions of interest, the dependence on the location of recording devices might be manifested. We compare the behavior of thunderstorms on the entire territory of the Russian Federation with those in the Baikal natural territory. We have established the power of thunderstorms over the Baikal region is lower. The daily variation in thunderstorm cells we obtained is consistent with the data from other works. There are no differences in other thunderstorm characteristics between the regions under study. This might be due to peculiarities of the analysis method. On the basis of the work performed, we propose sites for new points of our own lightning location network, as well as additional methods of cluster analysis.
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Melnikov, B. F., P. I. Averin, and E. A. Melnikova. "Intelligent processing of acoustic emission data based on cluster analysis." Journal of Physics: Conference Series 1236 (June 2019): 012044. http://dx.doi.org/10.1088/1742-6596/1236/1/012044.

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Rose, Rodrigo L., Tejas G. Puranik, and Dimitri N. Mavris. "Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives." Aerospace 7, no. 10 (2020): 143. http://dx.doi.org/10.3390/aerospace7100143.

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The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.
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Jung, Se-Hoon, Jong-Chan Kim, and Chun-Bo Sim. "Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 330. http://dx.doi.org/10.11591/ijece.v6i1.9334.

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Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.
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Jung, Se-Hoon, Jong-Chan Kim, and Chun-Bo Sim. "Prediction Data Processing Scheme using an Artificial Neural Network and Data Clustering for Big Data." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 1 (2016): 330. http://dx.doi.org/10.11591/ijece.v6i1.pp330-336.

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Various types of derivative information have been increasing exponentially, based on mobile devices and social networking sites (SNSs), and the information technologies utilizing them have also been developing rapidly. Technologies to classify and analyze such information are as important as data generation. This study concentrates on data clustering through principal component analysis and K-means algorithms to analyze and classify user data efficiently. We propose a technique of changing the cluster choice before cluster processing in the existing K-means practice into a variable cluster choice through principal component analysis, and expanding the scope of data clustering. The technique also applies an artificial neural network learning model for user recommendation and prediction from the clustered data. The proposed processing model for predicted data generated results that improved the existing artificial neural network–based data clustering and learning model by approximately 9.25%.
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Susanty, Aries, Bambang Purwanggono, Nia Budi Puspitasari, and Chellsy Allison. "Conjoint Analysis for Evaluation of Customer’s Preference of Analgesic Generic Medicines under Non-proprietary Names." E3S Web of Conferences 202 (2020): 12022. http://dx.doi.org/10.1051/e3sconf/202020212022.

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The main objective of this research is to get greater insight into the customer preferences in purchasing analgesic generic medicines under the non-proprietary name and to identify clusters with different preference structures. This research uses conjoint analysis (CA) and cluster analysis as data processing. This research collects the data through questionnaire from 200 respondents and uses the convenience sampling method to choose 200 respondents from sixteen districts in Semarang. The result of data processing with conjoint analysis indicated that customer prefers the analgesic generic medicine under the non-proprietary name with the following condition: the price of 20% of analgesic generic-branded minutes, has 15 minutes onset time of effect, can be purchased at minimarket, in the form of syrup, and the source of information is family and friend. Moreover, the result of data processing also indicated that the importance of attribute is the place of purchase, followed by price, onset time of drugs, the form of drugs, and, the source of information. Then, the result of data processing with clustering analysis indicated that the respondent can be grouped into four clusters. The attribute that has the highest importance level in cluster 1 until cluster 4 is ‘form of drugs’, ‘the place of purchase’, ‘source of information’, and ‘price’, respectively.
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Haryono Setiadi, Safira Nuri Safitri, and Esti Suryani. "Educational Data Mining Menggunakan Metode Analysis Cluster dan Decision Tree berdasarkan Log Mining." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 3 (2022): 448–56. http://dx.doi.org/10.29207/resti.v6i3.3935.

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Educational Data Mining (EDM) often appears to be applied in big data processing in the education sector. One of the educational data that can be further processed with EDM is activity log data from an e-learning system used in teaching and learning activities. The log activity can be further processed more specifically by using log mining. The purpose of this study was to process log data from the Sebelas Maret University Online Learning System (SPADA UNS) to determine student learning behavior patterns and their relationship to the final results obtained. The data mining method applied in this research is cluster analysis with the K-means Clustering and Decision Tree algorithms. The clustering process is used to find groups of students who have similar learning patterns. While the decision tree is used to model the results of the clustering in order to enable the analysis and decision-making processes. Processing of 11,139 SPADA UNS log data resulted in 3 clusters with a Davies Bouldin Index (DBI) value of 0.229. The results of these three clusters are modeled by using a Decision Tree. The decision tree model in cluster 0 represents a group of students who have a low tendency of learning behavior patterns with the highest frequency of access to course viewing activities obtained accuracy of 74.42% . In cluster 1, which contains groups of students with high learning behavior patterns, have a high frequency of access to viewing discussion activities obtained accuracy of 76.47%. While cluster 2 is a group of students who have a pattern of learning behavior that is having a high frequency of access to the activity of sending assignments obtained accuracy of 90.00%.
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Dissertations / Theses on the topic "Cluster analysis – Data processing"

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Zhang, Yiqun. "Advances in categorical data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/658.

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Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.
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Jia, Hong. "Clustering of categorical and numerical data without knowing cluster number." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1495.

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Yang, Bin, and 杨彬. "A novel framework for binning environmental genomic fragments." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B45789344.

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Li, Junjie. "Some algorithmic studies in high-dimensional categorical data clustering and selection number of clusters." HKBU Institutional Repository, 2008. http://repository.hkbu.edu.hk/etd_ra/1011.

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Lee, King-for Foris, and 李敬科. "Clustering uncertain data using Voronoi diagram." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B43224131.

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Ptitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.
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Van, Der Linde Byron-Mahieu. "A comparative analysis of the singer’s formant cluster." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/85563.

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Thesis (MMus)-- Stellenbosch University, 2013.<br>ENGLISH ABSTRACT: It is widely accepted that the singer’s formant cluster (Fs) – perceptual correlates being twang and ring, and pedagogically referred to as head resonance – is the defining trait of a classically trained voice. Research has shown that the spectral energy a singer harnesses in the Fs region can be measured quantitatively using spectral indicators Short-Term Energy Ratio (STER) and Singing Power Ratio (SPR). STER is a modified version of the standard measurement tool Energy Ratio (ER) that repudiates dependency on the Long-Term Average Spectrum (LTAS). Previous studies have shown that professional singers produce more Fs spectral energy when singing in ensemble mode than in solo mode; however for amateur singers, the opposite trend was noticed. Little empirical evidence in this regard is available concerning undergraduate vocal performance majors. This study was aimed at investigating the resonance tendencies of individuals from the latter target group, as evidenced when singing in two performance modes: ensemble and solo. Eight voice students (two per SATB voice part) were selected to participate. Subjects were recorded singing their parts individually, as well as in full ensemble. By mixing the solo recordings together, comparisons of the spectral content could be drawn between the solo and ensemble performance modes. Samples (n=4) were extracted from each piece for spectral analyses. STER and SPR means were highly proportional for both pieces. Results indicate that the singers produce significantly higher levels of spectral energy in the Fs region in ensemble mode than in solo mode for one piece (p<0.05), whereas findings for the other piece were insignificant. The findings of this study could inform the pedagogical approach to voice-training, and provides empirical bases for discussions about voice students’ participation in ensemble ventures.<br>AFRIKAANSE OPSOMMING: Dit word algemeen aanvaar dat die singer’s formant cluster (Fs) – die perseptuele korrelate is die Engelse “twang” en “ring”, en waarna daar in die pedagogie verwys word as kopresonansie – die bepalende eienskap is van ’n Klassiek-opgeleide stem. Navorsing dui daarop dat die spektrale energie wat ’n sanger in die Fs omgewing inspan kwantitatief gemeet kan word deur die gebruik van Short-Term Energy Ratio (STER) en Singing Power Ratio (SPR) as spektrale aanwysers. STER is ’n gewysigde weergawe van die standaard maatstaf vir energie in die Fs, naamlik Energy Ratio (ER), wat afhanklikheid van die Long-Term Average Spectrum (LTAS) verwerp. Vorige studies het getoon dat professionele sangers meer Fs energie produseer in ensemble konteks as in solo konteks, in teenstelling met amateur sangers waar die teenoorgestelde die norm is. Min empiriese data in hierdie verband is beskikbaar, m.b.t. voorgraadse uitvoerende sangstudente. Hierdie studie is daarop gemik om die tendense in resonansie by individue uit die laasgenoemde groep te ondersoek, soos dit blyk in die twee uitvoerende kontekste: ensemble en solo. Agt sangstudente (twee per SATB stemgroep) is geselekteer om aan die studie deel te neem. Die deelnemers het hul stempartye individueel en in volle ensemble gesing, en is by beide geleenthede opgeneem. Deur die soloopnames te meng, kon vergelykings van die spektrale inhoud gemaak word tussen die solo en ensemble konteks. ’n Steekproef (n=4) is uit elke stuk onttrek vir spektrale analise. Die STER en SPR gemiddeldes was eweredig vir beide stukke. Resultate toon dat die sangers beduidend hoër vlakke van spektrale energie in die Fs omgewing produseer in ensemble konteks as in solo konteks vir een stuk (p<0.05), terwyl die bevindinge vir die tweede stuk nie beduidend was nie. Die bevindinge van hierdie studie kan belangrik wees vir die pedagogiese benadering tot stemopleiding, en lewer empiriese basis vir gesprekke oor die betrokkenheid van sangstudente in die ensemble bedryf.
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Ramirez, Jon. "Analysis of compute cluster nodes with varying memory hierarchy distributions." To access this resource online via ProQuest Dissertations and Theses @ UTEP, 2009. http://0-proquest.umi.com.lib.utep.edu/login?COPT=REJTPTU0YmImSU5UPTAmVkVSPTI=&clientId=2515.

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Cole, Rowena Marie. "Clustering with genetic algorithms." University of Western Australia. Dept. of Computer Science, 1998. http://theses.library.uwa.edu.au/adt-WU2003.0008.

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Clustering is the search for those partitions that reflect the structure of an object set. Traditional clustering algorithms search only a small sub-set of all possible clusterings (the solution space) and consequently, there is no guarantee that the solution found will be optimal. We report here on the application of Genetic Algorithms (GAs) -- stochastic search algorithms touted as effective search methods for large and complex spaces -- to the problem of clustering. GAs which have been made applicable to the problem of clustering (by adapting the representation, fitness function, and developing suitable evolutionary operators) are known as Genetic Clustering Algorithms (GCAs). There are two parts to our investigation of GCAs: first we look at clustering into a given number of clusters. The performance of GCAs on three generated data sets, analysed using 4320 differing combinations of adaptions, establishes their efficacy. Choice of adaptions and parameter settings is data set dependent, but comparison between results using generated and real data sets indicate that performance is consistent for similar data sets with the same number of objects, clusters, attributes, and a similar distribution of objects. Generally, group-number representations are better suited to the clustering problem, as are dynamic scaling, elite selection and high mutation rates. Independent generalised models fitted to the correctness and timing results for each of the generated data sets produced accurate predictions of the performance of GCAs on similar real data sets. While GCAs can be successfully adapted to clustering, and the method produces results as accurate and correct as traditional methods, our findings indicate that, given a criterion based on simple distance metrics, GCAs provide no advantages over traditional methods. Second, we investigate the potential of genetic algorithms for the more general clustering problem, where the number of clusters is unknown. We show that only simple modifications to the adapted GCAs are needed. We have developed a merging operator, which with elite selection, is employed to evolve an initial population with a large number of clusters toward better clusterings. With regards to accuracy and correctness, these GCAs are more successful than optimisation methods such as simulated annealing. However, such GCAs can become trapped in local minima in the same manner as traditional hierarchical methods. Such trapping is characterised by the situation where good (k-1)-clusterings do not result from our merge operator acting on good k-clusterings. A marked improvement in the algorithm is observed with the addition of a local heuristic.
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Cui, Yingjie, and 崔英杰. "A study on privacy-preserving clustering." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2009. http://hub.hku.hk/bib/B4357225X.

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Books on the topic "Cluster analysis – Data processing"

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Analysis of longitudinal and cluster-correlated data. Institute of Mathematical Statistics, 2004.

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Moisl, Hermann. Cluster analysis for corpus linguistics. De Gruyter, 2015.

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C, Dubes Richard, ed. Algorithms for clustering data. Prentice Hall, 1988.

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Backer, E. Computer-assisted reasoning in cluster analysis. Prentice Hall, 1995.

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Mucha, Hans-Joachim. Clusteranalyse mit Mikrocomputern. Akademie Verlag, 1992.

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Cluster dissection and analysis: Theory, FORTRAN programs, examples. Horwood, 1985.

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Willett, Peter. Parallel database processing: Text retrieval and cluster analysis using the DAP. Pitman, 1990.

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Kaufman, Leonard. Finding groups in data: An introduction to cluster analysis. Wiley, 2005.

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Kaufman, Leonard. Finding groups in data: An introduction to cluster analysis. Wiley, 1990.

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Viattchenin, Dmitri A. A heuristic approach to possibilistic clustering: Algorithms and applications. Springer, 2013.

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Book chapters on the topic "Cluster analysis – Data processing"

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Bezdek, James C., James Keller, Raghu Krisnapuram, and Nikhil R. Pal. "Cluster Analysis for Object Data." In Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer US, 1999. http://dx.doi.org/10.1007/0-387-24579-0_2.

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Bezdek, James C., James Keller, Raghu Krisnapuram, and Nikhil R. Pal. "Cluster Analysis for Relational Data." In Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. Springer US, 1999. http://dx.doi.org/10.1007/0-387-24579-0_3.

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Geweniger, Tina, Frank-Michael Schleif, Alexander Hasenfuss, Barbara Hammer, and Thomas Villmann. "Comparison of Cluster Algorithms for the Analysis of Text Data Using Kolmogorov Complexity." In Advances in Neuro-Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03040-6_8.

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Klawonn, Frank. "Identifying Single Good Clusters in Data Sets." In Advances in Machine Vision, Image Processing, and Pattern Analysis. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11821045_17.

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Yu, Renwei, Mithila Nagendra, Parth Nagarkar, K. Selçuk Candan, and Jong Wook Kim. "Data-Utility Sensitive Query Processing on Server Clusters to Support Scalable Data Analysis Services." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19294-4_7.

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Caruso, Giulia, Adelia Evangelista, and Stefano Antonio Gattone. "Profiling visitors of a national park in Italy through unsupervised classification of mixed data." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-304-8.27.

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Cluster analysis has for long been an effective tool for analysing data. Thus, several disciplines, such as marketing, psychology and computer sciences, just to mention a few, did take advantage from its contribution over time. Traditionally, this kind of algorithm concentrates only on numerical or categorical data at a time. In this work, instead, we analyse a dataset composed of mixed data, namely both numerical than categorical ones. More precisely, we focus on profiling visitors of the National Park of Majella in the Abruzzo region of Italy, which observations are characterized by variables such as gender, age, profession, expectations and satisfaction rate on park services. Applying a standard clustering procedure would be wholly inappropriate in this case. Therefore, we hereby propose an unsupervised classification of mixed data, a specific procedure capable of processing both numerical than categorical variables simultaneously, releasing truly precious information. In conclusion, our application therefore emphasizes how cluster analysis for mixed data can lead to discover particularly informative patterns, allowing to lay the groundwork for an accurate customers profiling, starting point for a detailed marketing analysis.
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Li, Xianghua. "Simulation Analysis of the Life Cycle of the Tire Industry Cluster Based on the Complex Network." In Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2019). Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1468-5_196.

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Shi, Xuan. "Parallelizing Affinity Propagation Using Graphics Processing Units for Spatial Cluster Analysis over Big Geospatial Data." In Advances in Geocomputation. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-22786-3_32.

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McCreadie, Richard, John Soldatos, Jonathan Fuerst, et al. "Leveraging Data-Driven Infrastructure Management to Facilitate AIOps for Big Data Applications and Operations." In Technologies and Applications for Big Data Value. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78307-5_7.

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AbstractAs institutions increasingly shift to distributed and containerized application deployments on remote heterogeneous cloud/cluster infrastructures, the cost and difficulty of efficiently managing and maintaining data-intensive applications have risen. A new emerging solution to this issue is Data-Driven Infrastructure Management (DDIM), where the decisions regarding the management of resources are taken based on data aspects and operations (both on the infrastructure and on the application levels). This chapter will introduce readers to the core concepts underpinning DDIM, based on experience gained from development of the Kubernetes-based BigDataStack DDIM platform (https://bigdatastack.eu/). This chapter involves multiple important BDV topics, including development, deployment, and operations for cluster/cloud-based big data applications, as well as data-driven analytics and artificial intelligence for smart automated infrastructure self-management. Readers will gain important insights into how next-generation DDIM platforms function, as well as how they can be used in practical deployments to improve quality of service for Big Data Applications.This chapter relates to the technical priority Data Processing Architectures of the European Big Data Value Strategic Research &amp; Innovation Agenda [33], as well as the Data Processing Architectures horizontal and Engineering and DevOps for building Big Data Value vertical concerns. The chapter relates to the Reasoning and Decision Making cross-sectorial technology enablers of the AI, Data and Robotics Strategic Research, Innovation &amp; Deployment Agenda [34].
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Divjak, Dagmar, and Nick Fieller. "Cluster analysis." In Human Cognitive Processing. John Benjamins Publishing Company, 2014. http://dx.doi.org/10.1075/hcp.43.16div.

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Conference papers on the topic "Cluster analysis – Data processing"

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Cui, Guangcai, and Hongwei Gao. "Rough Set Processing Outliers in Cluster Analysis." In 2019 IEEE 4th International Conference on Cloud Computing and Big Data Analysis (ICCCBDA). IEEE, 2019. http://dx.doi.org/10.1109/icccbda.2019.8725708.

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Moskvichev, V. V., U. S. Postnikova, and O. V. Taseiko. "Cluster analysis and individual anthropogenic risk." In Spatial Data Processing for Monitoring of Natural and Anthropogenic Processes 2021. Crossref, 2021. http://dx.doi.org/10.25743/sdm.2021.54.88.063.

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Models and assessment methods of anthropogenic risk are analyzed at this article, general basis of mathematical approach for risk analysis is disclosed. Based on multivariate statistic methods, algorithm of analysis for Siberian territories safety is formulated, it allows to define acceptable level of risk for each territorial group (cities with population density more than 70 000, towns with population less than 70 000, and municipals areas).
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Yu, Zhanwu, Bin Hu, Zhongmin Li, and Zeng Wu. "GlobeSIGht: a geospace information system based on double-cluster architecture." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.808592.

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Pal, Amrit, and Sanjay Agrawal. "A Time Based Analysis of Data Processing on Hadoop Cluster." In 2014 International Conference on Computational Intelligence and Communication Networks (CICN). IEEE, 2014. http://dx.doi.org/10.1109/cicn.2014.136.

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Giurcaneanu, C. D., I. Tabus, I. Shmulevich, and Wei Zhang. "Stability-based cluster analysis applied to microarray data." In Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings. IEEE, 2003. http://dx.doi.org/10.1109/isspa.2003.1224814.

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Ma, Yingning. "Cluster analysis for cancer omics data using Neural Network with data augmentation." In SPML 2022: 2022 5th International Conference on Signal Processing and Machine Learning. ACM, 2022. http://dx.doi.org/10.1145/3556384.3556388.

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Li, Wei, Qiuqi Ruan, Gaoyun An, and Jun Wan. "Feature extraction of multimodal data by cluster-based correlation discriminative analysis." In 2012 11th International Conference on Signal Processing (ICSP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icosp.2012.6491702.

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Cebeci, Zeynel, and Cagatay Cebeci. "kpeaks: An R Package for Quick Selection of K for Cluster Analysis." In 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). IEEE, 2018. http://dx.doi.org/10.1109/idap.2018.8620896.

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Godara, Hanuman, M. C. Govil, and E. S. Pilli. "Performance Factor Analysis and Scope of Optimization for Big Data Processing on Cluster." In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC). IEEE, 2018. http://dx.doi.org/10.1109/pdgc.2018.8745857.

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Vats, Prashant, Manju Mandot, and Anjana Gosain. "A Comparative Analysis of Various Cluster Detection Techniques for Data Mining." In 2014 International Conference on Electronic Systems, Signal Processing and Computing Technologies (ICESC). IEEE, 2014. http://dx.doi.org/10.1109/icesc.2014.67.

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Reports on the topic "Cluster analysis – Data processing"

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Chandar, Bharat, Ali Hortaçsu, John List, Ian Muir, and Jeffrey Wooldridge. Design and Analysis of Cluster-Randomized Field Experiments in Panel Data Settings. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w26389.

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Fowler, Kimberly M., Alison H. A. Colotelo, Janelle L. Downs, et al. Simplified Processing Method for Meter Data Analysis. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1255411.

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Jelski, Daniel A., Z. C. Wu, and Thomas F. George. An Inquiry into the Structure of the Si60 Cluster: Analysis of Fragmentation Data. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada215488.

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Hodgkiss, W. S. Shallow Water Adaptive Array Processing and Data Analysis. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada306525.

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Boyd, Timothy J. Processing and Analysis of SCICEX-2000 CTD Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada628072.

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Boyd, Timothy. Processing and Analysis of SCICEX-2000 CTD Data. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada626128.

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Mayo, Jackson R., W. Philip, Jr Kegelmeyer, Matthew H. Wong, et al. A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data. Office of Scientific and Technical Information (OSTI), 2010. http://dx.doi.org/10.2172/992310.

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Spina, John F. Integrated RF Sensor Signal/Data Processing Information Analysis Center (IAC). Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada401075.

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Konovalov, Mikhail. Analysis of Industrial Software Solutions for Data Processing and Storage. Intellectual Archive, 2019. http://dx.doi.org/10.32370/iaj.2071.

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Cheng, Yi-Wen, and Christian L. Sargent. Data-reduction and analysis procedures used in NIST's thermomechanical processing research. National Institute of Standards and Technology, 1990. http://dx.doi.org/10.6028/nist.ir.3950.

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