Earticle

Granulation of information appears in many areas, such as machine learning, evidence theory, and data mining. Granular computing is the core research field in granulation of information. It is an effective tool for complex problem, massive data mining and fuzzy information processing. In the basis of principle of granularity, we aim to study the granular decomposing method in granules space based on rough set. Moreover, the criteria conditions for attribution necessity and attribute reduction are proposed. Finally, the corresponding equivalence is proved to traditional rough set theory. It will lay the foundation for attribute reduction under the granular representation in rough set.

Color image segmentation algorithms are proposed based on granular computing clustering (GrCC). Firstly, the atomic hyperspherical granule is represented as the vector including the RGB value of pixel of color image and radii 0. Secondly, the union operator of two hyperspherical granules is designed to obtain the larger hyperspherical granule compared with these two hyperspherical granules. Thirdly, the granular computing clustering is developed by the union operator and the user-defined granularity threshold . Global Consistency Error (GCE), Variation of Information (VI), Rand Index (RI), and Loss Entropy (ΔEn) are used to evaluate the segmentations. Segmentations of the color images selected from internet and BSD300 show that segmentations by GrCC speed up the segmentation process and achieve the better segmentation performance compared with Kmeans and FCM segmentations.

A new PCA algorithm is introduced, utilizing a rough cluster-based granulation scheme for segmentation of multivariate time series and process monitoring purposes. This granulated cluster-based algorithm can be used for segmentation of multivariate time series and initialization of other partitioning clustering methods that need to have good initialization parameters. The proposed algorithm is suitable for mining data sets, which are large both in dimension and size, in case generation. It utilizes Principal Component Analysis (PCA) specification and an innovative granular computing method for detection of changes in the hidden structure of multivariate time series data in a bottom up cluster merging manner. Rough set theory is used for feature extraction and solving superfluous attributes issue. The algorithm has been tested on an artificial case study. The resulting performances show the successful and promising capabilities of the proposed algorithm.

A bottle up hyperbox granular computing (HBGrC) is developed based on distance measure. Firstly, hyperbox granule is represented by the beginning point and the end point. Secondly, the distance measure between two hyperbox granules is defined by the beginning points and the end points. Thirdly, operations between two hyperbox granules are designed to the transformation between two hyperbox granule spaces with different granularities, HBGrC is developed by the join operator and the user-defined granularity threshold  on the basis of bottle up scheme. Experimental results shown that HBGrC achieved the better testing accuracies over the machine learning benchmark datasets.

Shape of granule is one of the important issues in granular computing classification problems and related to the classification accuracy, the number of granule, and the join process of two granules. A bottle up granular computing classification algorithm (BUGrC) is developed in the frame work of fuzzy lattices. Firstly, the granules are represented as 4 shapes, namely hyperdiamond granule, hypersphere granule, hypercube granule, and hyperbox granule. Secondly, the granule set is induced by the training set and the bottle up join operator. Thirdly, machine learning benchmark datasets are used to analyze and discuss the BUGrC with different shape granules.

A framework of granular computing clustering algorithms is proposed in the paper. Firstly, granules are represented as the normal forms, such the diamond granule in 2-domensional space and hyperdiamond granule in N-dimensional space, sphere granule in 2-dimensional space and hypersphere granule in N-dimensional space. Secondly, operations between two granules are designed to realize the transformation between two spaces with different granularities. Thirdly, the threshold of granularity is used to control the join process between two granules. The performance of granular computing algorithms is evaluated by the experimental results on the data sets selected from machine learning repository.

Concept lattice essentially describes the relationship between objects and attributes. The reduction of multi-value attribute concept lattice is a hot topic in the fields of information retrieval, knowledge discovery and data mining etc., while the granular computing emphasizes observing and analyzing the same problem from different granular worlds. It makes the complex problems around us be mapped to an easy to handle and more simple theory of calculation. The paper gives definitions of the concept granule and the compatible concept granular set by application of information granular and the granular of layered theory, and provides an algorithm to compute concept granular set through calculation of the compatible relationship. The paper further constructs the concept granule lattice, and then deletes the attribute of smaller contribution to concept granule. Through the comparison of the concept granule lattices, the multi-value attribute reduction could be achieved and the core attribute set in the formal context could be obtained. Instances could demonstrate the high efficiency and accuracy of this algorithm that is easier to realize through programming. Through the resolution of attribute, the calculation complexity could be reduced and the efficiency of calculation could be improved.

The purpose of this paper is to deal with the data in large document databases, analyzes the research trend of scientific papers and research hot spots. There is practical significance to promote the development of science and technology in China in order to provide effective methods and tools for the researchers.

Through the analysis of the relationship between current intelligence transportation and large data, after the theory of granular computing concepts involved in preliminary studies, For the current data mining of intelligent transportation ,we presents a Data Mining Model Of Intelligent Transportation Based on Granular Computing. Utilizing granular computing in data mining theory advantages, through constructing a new data mining model to solve the tlarge-scale, complexity, uncertainty and ambiguity problems of massive data from intelligent transportation.

There are various ways in classification methodologies of data mining such as neural networks but the result should be explicit and understandable and the classification rules be short and clear. Rough set theory is a effective technique in extracting knowledge from incomplete and inconsistent information and makes an offer classification and approximation by various attributes with effect. This paper discusses granularity of knowledge for reasoning of uncertain concepts by using generalized rough set approximations based on hierarchical granulation structure and uses hierarchical classification methodology that is more effective technique for classification by applying core to upper level. The consistency rules with minimal attributes is discovered and applied to classifying real data.

Knowledge representation realized by information granules is one of the essential facets of granular computing and an area of intensive research. Fuzzy clustering and clustering are general vehicles to realize formation of information granules. Granulation - degranulation paradigm is one of the schemes determining and quantifying functionality and knowledge representation capabilities of information granules. In this study, we augment this paradigm by forming and optimizing a collection of associations among original and transformed information granules. We discuss several transformation schemes and analyze their properties. A series of numeric experiments is provided using which we quantify the improvement of the degranulation mechanisms offered by the optimized transformation of information granules.

Granular Computing has emerged as a unified and coherent framework of designing, processing, and interpretation of information granules. Information granules are formalized within various frameworks such as sets (interval mathematics), fuzzy sets, rough sets, shadowed sets, probabilities (probability density functions), to name several the most visible approaches. In spite of the apparent diversity of the existing formalisms, there are some underlying commonalities articulated in terms of the fundamentals, algorithmic developments and ensuing application domains. In this study, we introduce two pivotal concepts: a principle of justifiable granularity and a method of an optimal information allocation where information granularity is regarded as an important design asset. We show that these two concepts are relevant to various formal setups of information granularity and offer constructs supporting the design of information granules and their processing. A suite of applied studies is focused on knowledge management in which case we identify several key categories of schemes present there.