Search results for "Hierarchical agglomerative clustering"
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A Measure of Polarization for Tourism: Evidence from Italian Destinations
2011
This paper proposes an index of polarization for tourism which links the axiomatic theory of Esteban and Ray with the classical hierarchical agglomerative clustering techniques. The index is aimed at analyzing the dynamics of the average length of stay across Italian destinations, and more specifically to detect whether the polarization within the set of clusters of places with similar values of the indicator has varied over time.
The Hierarchical Agglomerative Clustering with Gower index: a methodology for automatic design of OLAP cube in ecological data processing context
2015
In Press, Corrected Proof; International audience; The OLAP systems can be an improvement for ecological studies. In fact, ecology studies, follows and analyzes phenomenon across space and time and according to several parameters. OLAP systems can provide to ecologists browsing in a large dataset. One focus of the current research on OLAP system is the automatic design of OLAP cubes and of data warehouse schemas. This kind of works makes accessible OLAP technology to non information technology experts. But to be efficient, the automatic OLAP building must take into account various cases. Moreover the OLAP technology is based on the concept of hierarchy. Thereby the hierarchical clustering m…
Twister Tries
2015
Many commonly used data-mining techniques utilized across research fields perform poorly when used for large data sets. Sequential agglomerative hierarchical non-overlapping clustering is one technique for which the algorithms’ scaling properties prohibit clustering of a large amount of items. Besides the unfavorable time complexity of O(n 2 ), these algorithms have a space complexity of O(n 2 ), which can be reduced to O(n) if the time complexity is allowed to rise to O(n 2 log2 n). In this paper, we propose the use of locality-sensitive hashing combined with a novel data structure called twister tries to provide an approximate clustering for average linkage. Our approach requires only lin…