The Experts below are selected from a list of 24 Experts worldwide ranked by ideXlab platform
Mendívez Vásquez, Bruno Luis - One of the best experts on this subject based on the ideXlab platform.
-
Diseño de un modelo computacional de minería de datos multidimensional utilizando lattices conceptuales para la búsqueda de conocimiento en entornos OLAP
Universidad Nacional de Trujillo, 2019Co-Authors: Mendívez Vásquez, Bruno LuisAbstract:En esta tesis se presenta un modelo de minería de datos multidimensional (basado en la arquitectura de minería analítica en línea OLAM), el cual consiste en una solución de capa tres que cuenta con motores de análisis multidimensional y minería de datos ejecutándose en conjunto, con el fin de extraer reglas de asociación exactas o aproximadas a partir de la formalización de una consulta multidimensional basada en restricciones. Este modelo fue concebido como una manera de extraer conocimiento dirigido por el descubrimiento (reglas de asociación) a partir de un cubo de datos o datamart, el cual principalmente, tiene una estructura multidimensional con capacidad de extracción de conocimiento dirigido por hipótesis. Por tanto, este modelo de capa tres OLAM tiene la capacidad de ejecutar algoritmos de minería de datos dentro de un espacio de búsqueda reducido, resultado de una consulta basada en restricciones aplicada a un datamart. Este modelo de capa tres, a través de la formalización de una consulta basada en restricciones, reduce de manera significativa el espacio de búsqueda de un cubo de datos, identificando de la consulta, las dimensiones involucradas así como sus columnas y filtros, resultando en una tabla de datos o contexto multivaluado. Este contexto es después transformado a un contexto formal binario con el uso de escalas personalizadas para cada uno de los atributos multivaluados. A partir del contexto binario resultante, un iceberg concept lattice es derivado con la finalidad de identificar un conjunto de itemsets frecuentes, los cuales son el punto de partida para el proceso de minería de reglas de asociación. Finalmente, y de acuerdo a la especificación de un soporte y confianza mínimos, las bases de Duquenne-Guigues y de Luxenburger son presentadas como resultado de la consulta inicial para su posterior análisisTesisIn this thesis, a multidimensional data mining model is proposed (based on the online analytical mining OLAM architecture), which consists of a layer three solution with both multidimensional and data mining analytical engines working together in order to extract approximate or exact association rules from the formalization of a constraint-based multidimensional query. This model was conceived as a way to extract discovery-driven knowledge (i.e. association rules mining) from a data cube or datamart which primarily has a multidimensional structure with only hypothesis validation capabilities. Thus, this OLAM layer three model is able to execute data mining algorithms within a reduced search space result of a constraint-based query applied to a datamart. This layer three model, through the formalization of a constraint-based query, reduces significantly a cube’s search space by identifying from the query the dimensions involved as well as it’s columns and filters, resulting in a data table or Multivalued context. This context is later transformed into a formal binary context with the use of personalized scales for each and every Multivalued Attribute. From the resulting binary context, an iceberg concep lattice is derived in order to identify a set of frequent itemsets, which is the first step to the association rules mining process. Finally, and according to the specification of a minimum support and confidence, both Duquenne-Guigues and Luxenburger basis are extracted and presented as the result of the initial query for further analysi
Liu Jundan - One of the best experts on this subject based on the ideXlab platform.
-
Visualization of multi-valued Attribute association rules based on concept lattice
Journal of Computer Applications, 2013Co-Authors: Liu JundanAbstract:Considering the problems caused by the traditional association rules visualization approaches,including being unable to display the frequent pattern and relationships of items,unitary express,especially being not conducive to represent multi-schema association rules,a new visualizing algorithm for multi-valued association rules mining was proposed.It introduced the redefinition and classification of multi-valued Attribute data by using conceptual lattice and presented the Multivalued Attribute items of frequent itemset and association rules with concept lattice structure.This methodology was able to achieve frequent itemset visualization and multi-schema visualization of association rules,including the type of one to one,one to many,many to one,many to many and concept hierarchy.At last,the advantages of these new methods were illustrated with the help of experimental data obtained from demographic data of a province,and the source data visualization,frequent pattern and association relation visual representation of the demographic data were also achieved.The practical application analysis and experimental results prove that the schema has more excellent visual effects for frequent itemset display and authentical multi-schema association rules visualization.
Yong Sang - One of the best experts on this subject based on the ideXlab platform.
-
Fault diagnostic strategy of Multivalued Attribute system based on growing algorithm
Proceedings of the Institution of Mechanical Engineers Part O: Journal of Risk and Reliability, 2019Co-Authors: Heng Tian, Fuhai Duan, Liang Fan, Yong SangAbstract:Traditionally, fault diagnostic strategy is used to obtain the optimal test sequence for binary systems. Actually, a lot of systems are not binary systems, such as Multivalued Attribute systems. Tr...
Liu Feng - One of the best experts on this subject based on the ideXlab platform.
-
SQL-Based Multi-Value Multilayer Mining Association Rules Excavation
Computer Technology and Development, 2008Co-Authors: Liu FengAbstract:On the basis of the analysis of the present algorithms about data mining of association rules in relational database,proposes one new method of multilayered association rules excavation based on the structured inquiry language SQL.First introduce a new coding method of concept lamination for multi-valued Attributes discrete,then use SQL the inquiry sentence,combining Multivalued Attribute coding,to achieve multilayer mining association rules in relational database.Experiments show that this algorithm has many advantages,such as quickness,effectiveness,ease to develop,etc.
Feng Jiang - One of the best experts on this subject based on the ideXlab platform.
-
The Hierarchies of Multivalued Attribute Domains and Corresponding Applications in Data Mining
Wireless Communications and Mobile Computing, 2018Co-Authors: Feng JiangAbstract:In mobile computing, machine learning models for natural language processing (NLP) have become one of the most attractive focus areas in research. Association rules among Attributes are common knowledge patterns, which can often provide potential and useful information such as mobile users' interests. Actually, almost each Attribute is associated with a hierarchy of the domain. Given an relation and any cut on the hierarchy for every Attribute a, there is another rough relation , where . This paper will establish the connection between the functional dependencies in R and , propose the method for extracting reducts in , and demonstrate the implementation of proposed method on an application in data mining of association rules. The method for acquiring association rules consists of the following three steps: (1) translating natural texts into relations, by NLP; (2) translating relations into rough ones, by Attributes analysis or fuzzy k-means (FKM) clustering; and (3) extracting association rules from concept lattices, by formal concept analysis (FCA). Our experimental results show that the proposed methods, which can be applied directly to regular mobile data such as healthcare data, improved quality, and relevance of rules.
