Relevant bibliographies by topics / Multidimensional data mining

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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 'Multidimensional data mining'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Multidimensional data mining"

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Jiawei Han, L. V. S. Lakshmanan, and R. T. Ng. "Constraint-based, multidimensional data mining." Computer 32, no. 8 (1999): 46–50. http://dx.doi.org/10.1109/2.781634.

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Bimonte, Sandro, Lucile Sautot, Ludovic Journaux, and Bruno Faivre. "Multidimensional Model Design using Data Mining." International Journal of Data Warehousing and Mining 13, no. 1 (January 2017): 1–35. http://dx.doi.org/10.4018/ijdwm.2017010101.

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Designing and building a Data Warehouse (DW), and associated OLAP cubes, are long processes, during which decision-maker requirements play an important role. But decision-makers are not OLAP experts and can find it difficult to deal with the concepts behind DW and OLAP. To support DW design in this context, we propose: (i) a new rapid prototyping methodology, integrating two different DM algorithms, to define dimension hierarchies according to decision-maker knowledge; (ii) a complete UML Profile, to define a DW schema that integrates both the DM algorithms; (iii) a mapping process to transform multidimensional schemata according to the results of the DM algorithms; (iv) a tool implementing the proposed methodology; (v) a full validation, based on a real case study concerning bird biodiversity. In conclusion, we confirm the rapidity and efficacy of our methodology and tool in providing a multidimensional schema to satisfy decision-maker analytical needs.
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Zhang, Chao, and Jiawei Han. "Multidimensional Mining of Massive Text Data." Synthesis Lectures on Data Mining and Knowledge Discovery 11, no. 2 (March 21, 2019): 1–198. http://dx.doi.org/10.2200/s00903ed1v01y201902dmk017.

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Behnisch, Martin, and Alfred Ultsch. "Urban data-mining: spatiotemporal exploration of multidimensional data." Building Research & Information 37, no. 5-6 (November 2009): 520–32. http://dx.doi.org/10.1080/09613210903189343.

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Kim, Dae-In, Joon Park, Hong-Ki Kim, and Bu-Hyun Hwang. "Mining Association Rules in Multidimensional Stream Data." KIPS Transactions:PartD 13D, no. 6 (October 31, 2006): 765–74. http://dx.doi.org/10.3745/kipstd.2006.13d.6.765.

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Chung-Ching Yu and Yen-Liang Chen. "Mining sequential patterns from multidimensional sequence data." IEEE Transactions on Knowledge and Data Engineering 17, no. 1 (January 2005): 136–40. http://dx.doi.org/10.1109/tkde.2005.13.

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Pawliczek, Piotr, and Witold Dzwinel. "Interactive Data Mining by Using Multidimensional Scaling." Procedia Computer Science 18 (2013): 40–49. http://dx.doi.org/10.1016/j.procs.2013.05.167.

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Gundem, Gunes, Christian Perez-Llamas, Alba Jene-Sanz, Anna Kedzierska, Abul Islam, Jordi Deu-Pons, Simon J. Furney, and Nuria Lopez-Bigas. "IntOGen: integration and data mining of multidimensional oncogenomic data." Nature Methods 7, no. 2 (February 2010): 92–93. http://dx.doi.org/10.1038/nmeth0210-92.

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Dzemyda, Gintautas, Virginijus Marcinkevičius, and Viktor Medvedev. "WEB APPLICATION FOR LARGE-SCALE MULTIDIMENSIONAL DATA VISUALIZATION." Mathematical Modelling and Analysis 16, no. 1 (June 24, 2011): 273–85. http://dx.doi.org/10.3846/13926292.2011.580381.

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In this paper, we present an approach of the web application (as a service) for data mining oriented to the multidimensional data visualization. This paper focuses on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation of such a web application obtains a multidimensional data set and as a result produces a visualization of this data set. It also supports different configuration parameters of the data mining methods used. Parallel computation has been used in the proposed implementation to run the algorithms simultaneously on different computers.
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kumar, Santhosh, and E. Ramaraj. "A Hybrid Model for Mining Multidimensional Data Sets." International Journal of Computer Applications Technology and Research 2, no. 3 (May 1, 2013): 214–17. http://dx.doi.org/10.7753/ijcatr0203.1001.

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Dissertations / Theses on the topic "Multidimensional data mining"

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Torre, Fabrizio. "3D data visualization techniques and applications for visual multidimensional data mining." Doctoral thesis, Universita degli studi di Salerno, 2014. http://hdl.handle.net/10556/1561.

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2012 - 2013
Despite modern technology provide new tools to measure the world around us, we are quickly generating massive amounts of high-dimensional, spatialtemporal data. In this work, I deal with two types of datasets: one in which the spatial characteristics are relatively dynamic and the data are sampled at different periods of time, and the other where many dimensions prevail, although the spatial characteristics are relatively static. The first dataset refers to a peculiar aspect of uncertainty arising from contractual relationships that regulate a project execution: the dispute management. In recent years there has been a growth in size and complexity of the projects managed by public or private organizations. This leads to increased probability of project failures, frequently due to the difficulty and the ability to achieve the objectives such as on-time delivery, cost containment, expected quality achievement. In particular, one of the most common causes of project failure is the very high degree of uncertainty that affects the expected performance of the project, especially when different stakeholders with divergent aims and goals are involved in the project...[edited by author]
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Nimmagadda, Shastri Lakshman. "Ontology based data warehousing for mining of heterogeneous and multidimensional data sources." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/2322.

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Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals.
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Wu, Hao-cun, and 吳浩存. "A multidimensional data model for monitoring web usage and optimizing website topology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29528215.

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Peterson, Angela R. "Visual data mining Using parallel coordinate plots with K-means clustering and color to find correlations in a multidimensional dataset /." Instructions for remote access, 2009. http://www.kutztown.edu/library/services/remote_access.asp.

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Ding, Guoxiang. "DERIVING ACTIVITY PATTERNS FROM INDIVIDUAL TRAVEL DIARY DATA: A SPATIOTEMPORAL DATA MINING APPROACH." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1236777859.

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Li, Hsin-Fang. "DATA MINING AND PATTERN DISCOVERY USING EXPLORATORY AND VISUALIZATION METHODS FOR LARGE MULTIDIMENSIONAL DATASETS." UKnowledge, 2013. http://uknowledge.uky.edu/epb_etds/4.

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Oral health problems have been a major public health concern profoundly affecting people’s general health and quality of life. Given that oral health data is composed of several measurable dimensions including clinical measurements, socio-behavioral factors, genetic predispositions, self-reported assessments, and quality of life measures, strategies for analyzing multidimensional data are neither computationally straightforward nor efficient. Researchers face major challenges to identify tools that circumvent the processes of manually probing the data. The purpose of this dissertation is to provide applications of the proposed methodology on oral health-related data that go beyond identifying risk factors from a single dimension, and to describe large-scale datasets in a natural intuitive manner. The three specific applications focus on the utilization of 1) classification regression tree (CART) to understand the multidimensional factors associated with untreated decay in childhood, 2) network analyses and network plots to describe connectedness of concurrent co-morbid conditions for pediatric patients with autism receiving dental treatments under general anesthesia, and 3) random forests in addition to conventional adjusted main effects analyses to identify potential environmental risk factors and interactive effects for periodontitis. Compared to findings from the previous literature, the use of these innovative applications demonstrates overlapping findings as well as novel discoveries to the oral health knowledge. The results of this research not only illustrate that these data mining techniques can be used to improve the delivery of information into knowledge, but also provide new avenues for future decision making and planning for oral health-care management.
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Kucuktunc, Onur. "Result Diversification on Spatial, Multidimensional, Opinion, and Bibliographic Data." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1374148621.

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Foltýnová, Veronika. "Multidimenzionální analýza dat a zpracování analytického zobrazení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-376922.

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This thesis deals with the analysis and display of multidimensional data. In the theoretical part, the issue of data mining, its tasks and techniques, and a brief explanation of the terms Business Intelligence and data warehouse are presented. The issue of databases is also described in this thesis. Subsequently, the options for displaying multidimensional data are described. At the end of the theoretical part is briefly explained the problems of optical networks and especially the terms Gigabit passive optical network and its frame, because the data from the frames of this network will be displayed by an application. In the practical part, you can find creating a source database and an application to create a OLAP cube and display multidimensional data. This application is based on the theoretical knowledge of multidimensional databases and OLAP technology.
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Nunes, Santiago Augusto. "Análise espaço-temporal de data streams multidimensionais." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-17102016-152137/.

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Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia.
Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.
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Nieto, Erick Mauricio Gómez. "Projeção multidimensional aplicada a visualização de resultados de busca textual." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05122012-105730/.

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Usuários da Internet estão muito familiarizados que resultados de uma consulta sejam exibidos como uma lista ordenada de snippets. Cada snippet possui conteúdo textual que mostra um resumo do documento referido (ou página web) e um link para o mesmo. Esta representação tem muitas vantagens como, por exemplo, proporcionar uma navegação fácil e simples de interpretar. No entanto, qualquer usuário que usa motores de busca poderia reportar possivelmente alguma experiência de decepção com este modelo. Todavia, ela tem limitações em situações particulares, como o não fornecimento de uma visão geral da coleção de documentos recuperados. Além disso, dependendo da natureza da consulta - por exemplo, pode ser muito geral, ou ambígua, ou mal expressa - a informação desejada pode ser mal classificada, ou os resultados podem contemplar temas variados. Várias tarefas de busca seriam mais fáceis se fosse devolvida aos usuários uma visão geral dos documentos organizados de modo a refletir a forma como são relacionados, em relação ao conteúdo. Propomos uma técnica de visualização para exibir os resultados de consultas web que visa superar tais limitações. Ela combina a capacidade de preservação de vizinhança das projeções multidimensionais com a conhecida representação baseada em snippets. Essa visualização emprega uma projeção multidimensional para derivar layouts bidimensionais dos resultados da pesquisa, que preservam as relações de similaridade de texto, ou vizinhança. A similaridade é calculada mediante a aplicação da similaridade do cosseno sobre uma representação bag-of-words vetorial de coleções construídas a partir dos snippets. Se os snippets são exibidos diretamente de acordo com o layout derivado, eles se sobrepõem consideravelmente, produzindo uma visualização pobre. Nós superamos esse problema definindo uma energia funcional que considera tanto a sobreposição entre os snippets e a preservação da estrutura de vizinhanças como foi dada no layout da projeção. Minimizando esta energia funcional é fornecida uma representação bidimensional com preservação das vizinhanças dos snippets textuais com sobreposição mínima. A visualização transmite tanto uma visão global dos resultados da consulta como os agrupamentos visuais que refletem documentos relacionados, como é ilustrado em vários dos exemplos apresentados
Internet users are very familiar with the results of a search query displayed as a ranked list of snippets. Each textual snippet shows a content summary of the referred document (or web page) and a link to it. This display has many advantages, e.g., it affords easy navigation and is straightforward to interpret. Nonetheless, any user of search engines could possibly report some experience of disappointment with this metaphor. Indeed, it has limitations in particular situations, as it fails to provide an overview of the document collection retrieved. Moreover, depending on the nature of the query - e.g., it may be too general, or ambiguous, or ill expressed - the desired information may be poorly ranked, or results may contemplate varied topics. Several search tasks would be easier if users were shown an overview of the returned documents, organized so as to reflect how related they are, content-wise. We propose a visualization technique to display the results of web queries aimed at overcoming such limitations. It combines the neighborhood preservation capability of multidimensional projections with the familiar snippet-based representation by employing a multidimensional projection to derive two-dimensional layouts of the query search results that preserve text similarity relations, or neighborhoods. Similarity is computed by applying the cosine similarity over a bag-of-words vector representation of collection built from the snippets. If the snippets are displayed directly according to the derived layout they will overlap considerably, producing a poor visualization. We overcome this problem by defining an energy functional that considers both the overlapping amongst snippets and the preservation of the neighborhood structure as given in vii the projected layout. Minimizing this energy functional provides a neighborhood preserving two-dimensional arrangement of the textual snippets with minimum overlap. The resulting visualization conveys both a global view of the query results and visual groupings that reflect related results, as illustrated in several examples shown
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Books on the topic "Multidimensional data mining"

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Zhang, Chao, and Jiawei Han. Multidimensional Mining of Massive Text Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2.

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Adam, Schenker, ed. Graph-theoretic techniques for web content mining. Hackensack, N.J: World Scientific, 2005.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Springer International Publishing AG, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Han, Jiawei, and Chao Zhang. Multidimensional Mining of Massive Text Data. Morgan & Claypool Publishers, 2019.

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Grouping Multidimensional Data: Recent Advances in Clustering. Springer, 2006.

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Nicholas, Charles, Marc Teboulle, and Jacob Kogan. Grouping Multidimensional Data: Recent Advances in Clustering. Springer, 2010.

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(Editor), Jacob Kogan, Charles Nicholas (Editor), and Marc Teboulle (Editor), eds. Grouping Multidimensional Data: Recent Advances in Clustering. Springer, 2006.

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Hurter, Christophe. Image-Based Visualization: Interactive Multidimensional Data Exploration. Morgan & Claypool Publishers, 2016.

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Book chapters on the topic "Multidimensional data mining"

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Tao, Fangbo. "Multidimensional Summarization." In Multidimensional Mining of Massive Text Data, 91–116. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_6.

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Cyganek, Bogusław, and Michał Woźniak. "Efficient Multidimensional Pattern Recognition in Kernel Tensor Subspaces." In Data Mining and Big Data, 529–37. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_54.

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Zdunek, Rafał, and Michalina Kotyla. "Extraction of Dynamic Nonnegative Features from Multidimensional Nonstationary Signals." In Data Mining and Big Data, 557–66. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40973-3_57.

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Günzel, Holger, Jens Albrecht, and Wolfgang Lehner. "Data Mining in a Multidimensional Environment." In Advances in Databases and Information Systems, 191–204. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48252-0_15.

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Zhang, Chao, and Jiawei Han. "Introduction." In Multidimensional Mining of Massive Text Data, 1–10. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_1.

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Zhang, Chao, and Jiawei Han. "Topic-Level Taxonomy Generation." In Multidimensional Mining of Massive Text Data, 13–30. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_2.

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Zhang, Chao, and Jiawei Han. "Cross-Dimension Prediction in Cube Space." In Multidimensional Mining of Massive Text Data, 117–41. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-031-01914-2_7.

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Zhang, Yihao, Mehmet A. Orgun, Weiqiang Lin, and Rohan Baxter. "Mining Multidimensional Data through Element Oriented Analysis." In PRICAI 2008: Trends in Artificial Intelligence, 556–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_51.

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Duan, Jiuding, Jiyi Li, Yukino Baba, and Hisashi Kashima. "A Generalized Model for Multidimensional Intransitivity." In Advances in Knowledge Discovery and Data Mining, 840–52. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_65.

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Yang, Wen, Hao Wang, Yongfeng Cao, and Haijian Zhang. "Classification of Polarimetric SAR Data Based on Multidimensional Watershed Clustering." In Advanced Data Mining and Applications, 157–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_17.

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Conference papers on the topic "Multidimensional data mining"

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Dunstan, N., I. Despi, and C. Watson. "Anomalies in multidimensional contexts." In DATA MINING 2009. Southampton, UK: WIT Press, 2009. http://dx.doi.org/10.2495/data090181.

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Yokobayashi, Ryohei, and Takao Miura. "Multidimensional Data Mining Based on Tensor." In 2018 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2018. http://dx.doi.org/10.1109/icdmw.2018.00164.

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Pagani, Marco, Gloria Bordogna, and Massimiliano Valle. "Mining Multidimensional Data Using Clustering Techniques." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.112.

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Pagani, Marco, Gloria Bordogna, and Massimiliano Valle. "Mining Multidimensional Data Using Clustering Techniques." In 18th International Conference on Database and Expert Systems Applications (DEXA 2007). IEEE, 2007. http://dx.doi.org/10.1109/dexa.2007.4312921.

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Tsumoto, Shusaku, and Shoji Hirano. "Multidimensional temporal mining in clinical data." In the 2nd ACM SIGHIT symposium. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2110363.2110426.

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Patil, Pratima R., and Mamta Bhamare. "Multidimensional Data Mining for Anomaly Extraction." In 2013 Third International Conference on Advances in Computing and Communications (ICACC). IEEE, 2013. http://dx.doi.org/10.1109/icacc.2013.8.

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Cromp, Robert F., and William J. Campbell. "Data mining of multidimensional remotely sensed images." In the second international conference. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/170088.170397.

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Yokobayashi, Ryohei, and Takao Miura. "Multidimensional Data Mining Based on Tensor Model." In 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2018. http://dx.doi.org/10.1109/aike.2018.00031.

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Goil, S., and A. Choudhary. "High Performance Multidimensional Analysis and Data Mining." In SC98 - High Performance Networking and Computing Conference. IEEE, 1998. http://dx.doi.org/10.1109/sc.1998.10043.

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Assent, Ira, Ralph Krieger, Ralph Krieger, Boris Glavic, and Thomas Seidl. "Spatial Multidimensional Sequence Clustering." In Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdmw.2006.153.

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Reports on the topic "Multidimensional data mining"

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Traina, Agma, Caetano Traina, Spiros Papadimitriou, and Christos Faloutsos. Tri-Plots: Scalable Tools for Multidimensional Data Mining. Fort Belvoir, VA: Defense Technical Information Center, January 2001. http://dx.doi.org/10.21236/ada459873.

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