11,387 research outputs found
Mining top-k granular association rules for recommendation
Recommender systems are important for e-commerce companies as well as
researchers. Recently, granular association rules have been proposed for
cold-start recommendation. However, existing approaches reserve only globally
strong rules; therefore some users may receive no recommendation at all. In
this paper, we propose to mine the top-k granular association rules for each
user. First we define three measures of granular association rules. These are
the source coverage which measures the user granule size, the target coverage
which measures the item granule size, and the confidence which measures the
strength of the association. With the confidence measure, rules can be ranked
according to their strength. Then we propose algorithms for training the
recommender and suggesting items to each user. Experimental are undertaken on a
publicly available data set MovieLens. Results indicate that the appropriate
setting of granule can avoid over-fitting and at the same time, help obtaining
high recommending accuracy.Comment: 12 pages, 5 figures, submitted to Advances in Granular Computing and
Advances in Rough Sets, 2013. arXiv admin note: substantial text overlap with
arXiv:1305.137
Comparing Rough Set Theory with Multiple Regression Analysis as Automated Valuation Methodologies
This paper focuses on the problem of applying rough set theory to mass appraisal. This methodology was first introduced by a Polish mathematician, and has been applied recently as an automated valuation methodology by the author. The method allows the appraiser to estimate a property without defining econometric modeling, although it does not give any quantitative estimation of marginal prices. In a previous paper by the author, data were organized into classes prior to the valuation process, allowing for the if-then, or right “rule” for each property class to be defined. In that work, the relationship between property and class of valued was said to be dichotomic.mass appraisal; property valuation; rough set theory; valued tolerance relation
Sistemas granulares evolutivos
Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia ElĂ©trica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. MĂ©todos e algoritmos tĂŞm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princĂpio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação Ă© uma tĂ©cnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparĂŞncia, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho sĂŁo considerados dados numĂ©ricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares Ă© baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem Ă© particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de sĂ©ries temporais e controle usando dados sintĂ©ticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados nĂŁo-estacionários com mudanças graduais e abruptas de regime Ă© tambĂ©m analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saĂda. As abordagens e o paradigma introduzidos constituem uma extensĂŁo natural de sistemas inteligentes evolutivos para processamento de dados numĂ©ricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia ElĂ©tric
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