The devastating complexity of decision making in severe dynamic competitive environment of the universe, has forced the wise managers to have relevant strategic plans for their firms. In this paper, a new approach by ...The devastating complexity of decision making in severe dynamic competitive environment of the universe, has forced the wise managers to have relevant strategic plans for their firms. In this paper, a new approach by utilizing Mahalanobis-Taguchi System (MTS) and clustering algorithm in formulating the strategy has been proposed. In this approach, first by performing environmental analysis all internal and external factors affecting organization will be listed. Then the long range goals will be identified by top managers. By applying MTS the main set of factors affecting goals will come out. By identifying main factors, the goal-factor matrix will be formed. At this stage, by using clustering algorithm the proper clusters containing goals and factors influencing them will be constructed. Finally, from the created clusters the appropriate strategies would be generated. The advantage of applying this method is its accuracy and ease of applications in the environment with plenty of goals and numerous factors with interactions among them.展开更多
Classical Mahalanobis distance is used as a method of detecting outliers, and is affected by outliers. Some robust Mahalanobis distance is proposed via the fast MCD estimator. However, the bias of the MCD estimator in...Classical Mahalanobis distance is used as a method of detecting outliers, and is affected by outliers. Some robust Mahalanobis distance is proposed via the fast MCD estimator. However, the bias of the MCD estimator increases significantly as the dimension increases. In this paper, we propose the improved Mahalanobis distance based on a more robust Rocke estimator under high-dimensional data. The results of numerical simulation and empirical analysis show that our proposed method can better detect the outliers in the data than the above two methods when there are outliers in the data and the dimensions of data are very high.展开更多
文摘The devastating complexity of decision making in severe dynamic competitive environment of the universe, has forced the wise managers to have relevant strategic plans for their firms. In this paper, a new approach by utilizing Mahalanobis-Taguchi System (MTS) and clustering algorithm in formulating the strategy has been proposed. In this approach, first by performing environmental analysis all internal and external factors affecting organization will be listed. Then the long range goals will be identified by top managers. By applying MTS the main set of factors affecting goals will come out. By identifying main factors, the goal-factor matrix will be formed. At this stage, by using clustering algorithm the proper clusters containing goals and factors influencing them will be constructed. Finally, from the created clusters the appropriate strategies would be generated. The advantage of applying this method is its accuracy and ease of applications in the environment with plenty of goals and numerous factors with interactions among them.
文摘Classical Mahalanobis distance is used as a method of detecting outliers, and is affected by outliers. Some robust Mahalanobis distance is proposed via the fast MCD estimator. However, the bias of the MCD estimator increases significantly as the dimension increases. In this paper, we propose the improved Mahalanobis distance based on a more robust Rocke estimator under high-dimensional data. The results of numerical simulation and empirical analysis show that our proposed method can better detect the outliers in the data than the above two methods when there are outliers in the data and the dimensions of data are very high.
