Interpretations of Association Rules by Granular Computing

We present interpretations for association rules. We first introduce Pawlak's method, and the corresponding algorithm of finding decision rules (a kind of association rules). We then use extended random sets to present a new algorithm of finding interesting rules. We prove that the new algorithm is faster than Pawlak's algorithm. The extended random sets are easily to include more than one criterion for determining interesting rules. We also provide two measures for dealing with uncertainties in association rules

Knowledge Engineering from Data Perspective: Granular Computing Approach

The concept of rough set theory is a mathematical approach to uncertainly and vagueness in data analysis, introduced by Zdzislaw Pawlak in 1980s. Rough set theory assumes the underlying structure of knowledge is a partition. We have extended Pawlak’s concept of knowledge to coverings. We have taken a soft approach regarding any generalized subset as a basic knowledge. We regard a covering as basic knowledge from which the theory of knowledge approximations and learning, knowledge dependency and reduct are developed

An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Steady state properties of a mean field model of driven inelastic mixtures

We investigate a Maxwell model of inelastic granular mixture under the influence of a stochastic driving and obtain its steady state properties in the context of classical kinetic theory. The model is studied analytically by computing the moments up to the eighth order and approximating the distributions by means of a Sonine polynomial expansion method. The main findings concern the existence of two different granular temperatures, one for each species, and the characterization of the distribution functions, whose tails are in general more populated than those of an elastic system. These analytical results are tested against Monte Carlo numerical simulations of the model and are in general in good agreement. The simulations, however, reveal the presence of pronounced non-gaussian tails in the case of an infinite temperature bath, which are not well reproduced by the Sonine method.Comment: 23 pages, 10 figures, submitted for publicatio

Deformation-Driven Diffusion and Plastic Flow in Two-Dimensional Amorphous Granular Pillars

We report a combined experimental and simulation study of deformation-induced diffusion in compacted two-dimensional amorphous granular pillars, in which thermal fluctuations play negligible role. The pillars, consisting of bidisperse cylindrical acetal plastic particles standing upright on a substrate, are deformed uniaxially and quasistatically by a rigid bar moving at a constant speed. The plastic flow and particle rearrangements in the pillars are characterized by computing the best-fit affine transformation strain and non-affine displacement associated with each particle between two stages of deformation. The non-affine displacement exhibits exponential crossover from ballistic to diffusive behavior with respect to the cumulative deviatoric strain, indicating that in athermal granular packings, the cumulative deviatoric strain plays the role of time in thermal systems and drives effective particle diffusion. We further study the size-dependent deformation of the granular pillars by simulation, and find that different-sized pillars follow self-similar shape evolution during deformation. In addition, the yield stress of the pillars increases linearly with pillar size. Formation of transient shear lines in the pillars during deformation becomes more evident as pillar size increases. The width of these elementary shear bands is about twice the diameter of a particle, and does not vary with pillar size.Comment: 14 pages, 11 figure

Iterative Information Granulation for Novelty Detection in Complex Datasets

Recognition memory in a number of mammals is usually utilised to identify novel objects that violate model predictions. In humans in particular, the recognition of novel objects is foremost associated to their ability to group objects that are highly compatible/similar. Granular computing not only mimics the human cognition to draw objects together but also mimics the ability to capture associated properties by similarity, proximity or functionality. In this paper, an iterative information granulation approach is presented, for the problem of novelty detection in complex data. Two granular compatibility measures are used, based on principles of Granular Computing, namely the multidimensional distance between the granules, as well as the granular density and volume. A two-stage iterative information granulation is proposed in this work. In the first stage, a predefined number of granular detectors are constructed. The granular detectors capture the relationships (rules) between the input-output data and then use this information in a second granulation stage in order to discriminate new samples as novel. The proposed iterative information granulation approach for novelty detection is then applied to three different benchmark problems in pattern recognition demonstrating very good performance

NEPTUNE_CFD High Parallel Computing Performances for Particle-Laden Reactive Flows

This paper presents high performance computing of NEPTUNE_CFD V1.07@Tlse. NEPTUNE_CFD is an unstructured parallelized code (MPI) using unsteady Eulerian multi-fluid approach for dilute and dense particle-laden reactive flows. Three-dimensional numerical simulations of two test cases have been carried out. The first one, a uniform granular shear flow exhibits an excellent scalability of NEPTUNE_CFD up to 1024 cores, and demonstrates the good agreement between the parallel simulation results and the analytical solutions. Strong scaling and weak scaling benchmarks have been performed. The second test case, a realistic dense fluidized bed shows the code computing performances on an industrial geometry

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