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As innovation becomes important and complex, researchers started to explore innovation process under the background of Big Data. Technology Delivery System (TDS), a systematic method dynamically showing innovation process, has caused the extensive concern worldwide. As an essential step to construct TDS better, this study aims to identify main delivery actors in TDS based on multi-data sources, then...
A systematic framework for solving multiobjective optimization problems (MOPs) is proposed. A physical problem with multiple objectives is first transformed into an MOP. Multiobjective evolutionary algorithms are then applied. By solving the MOP, a set of Pareto optimal solutions and an approximate Pareto front can be obtained. To select a final solution, multicriteria decision making approaches are...
Innovations around "Big Data" can be characterized in terms of rapid technology development and deployment dynamics. For this purpose, combining "tech mining" (extraction of usable intelligence) from publication and patent databases with tech mining of business-related databases can elucidate activities and interests of business communities regarding Big Data innovation pathways...
Rapidly increasing competition of technological revolution drive all participants in the market to pay attention to the prospect of New and Emerging Science & Technologies (NESTs), which are newly invented, fast changing and developing, and have relatively limited applications in the marketplace. Therefore, a systematic method to evaluate the commercial potential of these NESTs is essential for...
The impacts of anti-takeover of public listed company on the target company, stockholders, managers and employees have two sides. To the target, anti-takeover can keep the target's independence, which benefit to long-term development of the target. While anti-takeover increases costs and expenses of the target, which make against current development of the target. To the shareholders of the target,...
So far, the K-means algorithm is the most widely used method for discovering clusters in data, and it has been used extensively in the commercial field, such as customer analysis. However, the efficiency of the algorithm needs to be improved when faced with large amounts of data. The improved algorithm avoids unnecessary calculations by using the triangle inequality. We applies the improved algorithm...
Nowadays, clustering algorithms are widely used in the commercial field, such as customer analysis, and this application has achieved good effect. K-means algorithm is by far the most commonly used method for clustering. Although, the time consumption is fairly high when faced with lager-scale data. In this paper, we improved the K-means algorithm. Our improvement is based on the triangle inequality...
Fuzzy Cognitive Maps (FCM) is an important graphic mean of representing causal relationship between concepts and analyzing inference patterns. In existing FCM, the value of the node which reflects the degree of activation of the concept fails to represent the multi-state nature of the concept. In order to eliminate the drawbacks from the existing FCM model, we for first time propose A Quantum Cognitive...
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