...Hofstede’s Six Cultural Dimensions Princess Smith BUS 600 Management Communications with Technology Tools Instructor: Brian Shaw March 10, 2013 Hofstede’s Six Cultural Dimensions “Hofstede’s research has been instrumental in furthering an understanding of cross-cultural management theory and practice, revealing that members of different societies hold divergent values concerning the nature of organizations and interpersonal relationship within them.” (Fernandez, Carlson, Stepina, & Nicholson, 1997). His work involves the identification of key work-related dimensions of national culture and six cultural dimensions such as power distance, uncertainty avoidance, individualism, masculinity, pragmatism, and indulgence. “Conducting business in today's modern business environment presents exciting opportunities for businesses and individuals.” (Baack, 2012). These dimensions, when considered together, were viewed as providing a framework for understanding how a culture resolves some of their most basic problems of life within organizations. Using the Hofstede Center, I did a cultural survey report on Australia and China which happens to be two of my favorite countries; one of which I have visited in the past. This type of report can be helpful in comparing the U.S.’s culture to others as well and how the U.S.’s culture can influence the way I understand the world around me. Culture has a very significant influence of people, especially managers and executives in...
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...clustered together based on the similarity in the partitions obtained. 35 algorithms were divided into 5 groups based on the partitions of 12 data sets. There can be other measures as well which can be used to cluster the algorithms. Comparisons can be done at a theoretical level as well, where objective functions of different algorithms are compared. The difference between clustering method and clustering algorithm is that method "is a general strategy used to solve a clustering problem", while algorithm "is an instance of a method". The most important thing that one needs to understand is that there is no best clustering algorithm. It all depends on the data and model used. Admissibility analysis of clustering algorithms: Fisher and vanNess defined certain admissibility criteria’s for clustering algorithms. They are convex, Cluster Proportion, Cluster Omission and Monotone. Klienberg, also gave three criteria, scale invariance, richness and consistency for the same problem. He also said that it is impossible to construct and algorithm which satisfies all of the three criteria. 4. Trends in data clustering Unstructured data has no relationship within objects. Examples: images, text, audio, video etc. They don't follow any specific format. Structured data on the other hand has semantic relationships within objects. Most clustering approaches use a vector based feature representation, instead of the structures in the object. Clustering ensembles This method earlier used for...
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