SCALABLE APPROXIMATION OF KERNEL FUZZY C-MEANS

Virtually every sector of business and industry that uses computing, including financial analysis, search engines, and electronic commerce, incorporate Big Data analysis into their business model. Sophisticated clustering algorithms are popular for deducing the nature of data by assigning labels to unlabeled data. We address two main challenges in Big Data. First, by definition, the volume of Big Data is too large to be loaded into a computer’s memory (this volume changes based on the computer used or available, but there is always a data set that is too large for any computer). Second, in real-time applications, the velocity of new incoming data prevents historical data from being stored and future data from being accessed. Therefore, we propose our Streaming Kernel Fuzzy c-Means (stKFCM) algorithm, which reduces both computational complexity and space complexity significantly. The proposed stKFCM only requires O(n2) memory where n is the (predetermined) size of a data subset (or data chunk) at each time step, which makes this algorithm truly scalable (as n can be chosen based on the available memory). Furthermore, only 2n2 elements of the full N × N (where N >> n) kernel matrix need to be calculated at each time-step, thus reducing both the computation time in producing the kernel elements and also the complexity of the FCM algorithm. Empirical results show that stKFCM, even with relatively very small n, can provide clustering performance as accurately as kernel fuzzy c-means run on the entire data set while achieving a significant speedup.

Decentralized or centralized production : impacts to the environment, industry, and the economy

Since product take-back is mandated in Europe, and has effects for producers worldwide including the U.S., designing efficient forward and reverse supply chain networks is becoming essential for business viability. Centralizing production facilities may reduce costs but perhaps not environmental impacts. Decentralizing a supply chain may reduce transportation environmental impacts but increase capital costs. Facility location strategies of centralization or decentralization are tested for companies with supply chains that both take back and manufacture products. Decentralized and centralized production systems have different effects on the environment, industry and the economy. Decentralized production systems cluster suppliers within the geographical market region that the system serves. Centralized production systems have many suppliers spread out that meet all market demand. The point of this research is to help further the understanding of company decision-makers about impacts to the environment and costs when choosing a decentralized or centralized supply chain organizational strategy. This research explores; what degree of centralization for a supply chain makes the most financial and environmental sense for siting facilities; and which factories are in the best location to handle the financial and environmental impacts of particular processing steps needed for product manufacture. This research considered two examples of facility location for supply chains when products are taken back; the theoretical case involved shoe resoling and a real world case study considered the location of operations for a company that reclaims multiple products for use as material inputs. For the theoretical example a centralized strategy to facility location was optimal: whereas for the case study a decentralized strategy to facility location was best. In conclusion, it is not possible to say that a centralized or decentralized strategy to facility location is in general best for a company that takes back products. Each company’s specific concerns, needs, and supply chain details will determine which degree of centralization creates the optimal strategy for siting their facilities.

Our Lives, Our Thoughts and Our Allegiance: New Immigrants and American Industry in 1914

Through comparative analysis of the immigrant labor forces at work in iron mining in northern Minnesota, coal mining in Illinois, and steel milling in the Calumet region of Chicago and Gary, this paper addresses the forms of social distance separating and marginalizing new immigrants from American society and trade unionism that existed in 1914, the year that marked the end point of mass immigration from Eastern and Southern Europe. The “new immigration” was a labor migration that congregated its subjects overwhelmingly in what were called "unskilled" or "semi-skilled" forms of labor. Skilled work was largely, with certain variations, the preserve of "American" or old immigrant workers. This labor gulf separating new immigrants and American workers was hardened by a spatial separateness. New immigrants often lived in what have been called industrial villages—the mining town or location, the factory neighborhood— striking in their isolation and insularity from mainstream society. This separateness and insularity became a major preoccupation for corporate managers, Progressive reformers, and for American trade unions as new immigrants began to engage in major labor struggles leading up to 1914. But among the three industries, only the union of coal miners, the United Mine Workers, enjoyed success in organizing the new immigrants. In the steel mills and the iron mines, the unions were either rooted out or failed to gain a foothold at all. The explanation for these differences is to be found in the different forms of industrial development among the industries studied.