Agrupamento subtrativo baseado em Kernel para dados simbólicos da natureza intervalar

In this work, we present extensions for known subtractive clustering methods. The subtractive clustering method for symbolic interval data (iSBC) as an extension of the subtractive clustering method developed by Chiu (1994), as well as the kernelbased subtractive clustering methods defined by one or two components for symbolic interval data (iKSBC1C and iKSBC2C, respectively) as extensions of a kernel-based subtractiveclusteringmethodproposedbyKimetal. (2005). Inaddition, sixstrategies will be proposed: the centroids of the proposed methods will be given as inputs to the methods K-means for interval data based on L2 distance proposed by De Carvalho, Brito and Bock (2006) (iKM+iSBC, iKM+iKSBC1C and iKM+iKSBC2C) and kernel K-means for symbolic data of the interval-valued developed by Costa (2011) (iKKM+iSBC, iKKM+iKSBC1C and iKKM+iKSBC2C) as a way to minimize the sensitivity of these methods to the choice of the centroid for de nition of the initial partition. Experiments using real data showed that the proposed kernelbased subtractive clustering methods (iSBC1C and iSBC2C) obtained better performance than the iSBC method, as well as the K-means (iKM+iSBC, iKM+iKSBC1C and iKM+iKSBC2C) and kernel K-means (iKKM+iSBC, iKKM+iKSBC1C and iKKM+iKSBC2C) methods, both for symbolic data interval-valued, using the centroids of methods proposed as inputs for them also obtained better performance that the iKM and iKKM methods.Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESApresenta-se, nesta dissertação, extensões de métodos de agrupamento subtrativoconhecidos. Ométododeagrupamentosubrativoparadadossimbólicosdenatureza intervalar (iSBC) é uma extensão do método de agrupamento subtrativo desenvolvido por Chiu (1994), já os métodos de agrupamento subtrativo baseados em kernel de nidos por uma ou duas componentes para dados simbólicos de natureza intervalar (iKSBC1C e iKSBC2C, respectivamente) são extensões do método de agrupamento subtrativo baseado em kernel proposto por Kim et al. (2005). Além disso, serão propostas seis estratégias: os centróides dos métodos propostos serão dados como entradas para os métodos K-médias para dados do tipo intervalo baseado em distância L2 proposto por De Carvalho, Brito e Bock (2006) (iKM+iSBC, iKM+iKSBC1C e iKM+iKSBC2C) e kernel K-médias para dados simbólicos do tipo intervalo, desenvolvido por Costa (2011) (iKKM+iSBC, iKKM+iKSBC1C e iKKM+iKSBC2C), como forma de minimizar a sensibilidade que esses métodos tem em relação a escolha do centróide para de nição da partição inicial. Experimentos utilizando dados reais mostraram que os métodos subtrativos baseados em kernel propostos (iKSBC1C e iKSBC2C) obtiveram melhor desempenho que o método iSBC, além disso os métodos K-médias (iKM+iSBC, iKM+iKSBC1C e iKM+iKSBC2C) e kernel K-médias (iKKM+iSBC, iKKM+iKSBC1C e iKKM+iKSBC2C), ambos para dados simbólicos do tipo intervalo, utilizando os centróides dos métodos propostos como entradas obtiveram melhor desempenho que os métodos iKM e iKKM

A New Approach to Adaptive Neuro-fuzzy Modeling using Kernel based Clustering

Data clustering is a well known technique for fuzzy model identification or fuzzy modelling for apprehending the system behavior in the form of fuzzy if-then rules based on experimental data Fuzzy c- Means FCM clustering and subtractive clustering SC are efficient techniques for fuzzy rule extraction in fuzzy modeling of Adaptive Neuro-fuzzy Inference System ANFIS In this paper we have employed a novel technique to build the rule base of ANFIS based on the kernel based variants of these two clustering techniques which have shown better clustering accuracy In kernel based clustering approach the kernel functions are used to calculate the distance measure between the data points during clustering which enables to map the data to a higher dimensional space This generalization makes data set more distinctly separable which results in more accurate cluster centers and therefore a more precise rule base for the ANFIS can be constructed which increases the prediction performance of the system The performance analysis of ANFIS models built using kernel based FCM and kernel based SC has been done on three business prediction problems viz sales forecasting stock price prediction and qualitative bankruptcy prediction A performance comparison with the ANFIS models based on conventional SC and FCM clustering for each of these forecasting problems has been provided and discusse

Clustering Learning for Robotic Vision

We present the clustering learning technique applied to multi-layer feedforward deep neural networks. We show that this unsupervised learning technique can compute network filters with only a few minutes and a much reduced set of parameters. The goal of this paper is to promote the technique for general-purpose robotic vision systems. We report its use in static image datasets and object tracking datasets. We show that networks trained with clustering learning can outperform large networks trained for many hours on complex datasets.Comment: Code for this paper is available here: https://github.com/culurciello/CL_paper1_cod

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM)clusteringwere also used to developmodels for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although themajority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the othermodels. The ANFISmodels have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season