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The large volume of big data makes it impractical for traditional clustering algorithms which are usually designed for entire data set. The purpose of this paper is to focus on incremental clustering which divides data into series of data chunks and only a small amount of data need to be clustered at each time. Few researches on incremental clustering algorithm address the problem of optimizing cluster center initialization for each data chunk and selecting multiple passing points for each cluster.

Through optimizing initial cluster centers, quality of clustering results is improved for each data chunk and then quality of final clustering results is enhanced. Moreover, through selecting multiple passing points, more accurate information is passed down to improve the final clustering results. The method has been proposed to solve those two problems and is applied in the proposed algorithm based on streaming kernel fuzzy c-means (stKFCM) algorithm.

Experimental results show that the proposed algorithm demonstrates more accuracy and better performance than streaming kernel stKFCM algorithm.

This paper addresses the problem of improving the performance of increment clustering through optimizing cluster center initialization and selecting multiple passing points. The paper analyzed the performance of the proposed scheme and proved its effectiveness.

Until now, identification of three‐dimensional non‐convex (concave) blocks has not been accomplished. However, a direct body concept, which is introduced here, can deal with both convex and concave blocks in the same process in connection with detection of individual blocks and computation of physical quantities. Thus, the dilemma in the generalization of identifying three‐dimensional multi‐block systems has been eliminated. The directed body concept used in geometrical identification problems makes it possible to build a novel automatic identification system for three‐dimensional multi‐block systems. This new system eliminates the time‐consuming work on geometrical identification, and copes with a variety of applications in multi‐body systems, such as rock masses.

The assumption that the family migrates as a unit downplays migrants’ circularity. This chapter focuses on China's rural–urban labor migrants that travel back and forth between the sites of work and home community and between places of work. I argue that migrants and their households pursue work flexibility in order to obtain the best of the urban and rural worlds, by gaining earnings from urban work and at the same time maintaining social and economic security in the countryside. Work flexibility demands flexibility in household organization, in the form of division of labor and collaboration between genders, generations, and households. Based on a study in Sichuan, I examine household biographies and narratives to identify migrants’ work and household strategies.

Migrants change jobs frequently, switch from one type of work to another and one location to another readily, and often return to the home village for months or even years before pursuing migrant work again. Not only are migrants ready to split the household between the city and the countryside, but also they frequently change from one form of division of labor to another. The inside–outside model, where the wife stays in the village and the husband does migrant work, used to be the dominant arrangement. Over time, the outside–outside model, where both the husband and wife migrate to work and leave behind other family members, is increasingly popular. This is facilitated by intergenerational and interhousehold division of labor in the form of assistance by the extended family. Intergenerational division of labor takes place when the second generation is replacing the parents in migrant work. This research's findings support the notion that rural–urban migrants are fast becoming a hybrid segment of Chinese society, playing dual roles of farmers and urban workers and straddling the peasant and urban worlds.