02349nas a2200193   4500008004100000245008200041210006900123260000900192300001200201490000700213520168700220653002101907653002801928653002701956653002901983100001302012700001302025856011702038       2013                        eng d00aHierarchical clustering analysis method based on the grid with obstacle space0 aHierarchical clustering analysis method based on the grid with o  c2013  a76 - 820 v113 aThe advantage of grid-based clustering method is its fast processing speed. The speed of clustering algorithm and the number of data objects is unrelated. To discover any size and shape of the clusterÿit is by the number of units on each dimension in the data space. In this method, the amount of data and computation time does not matter, calculations and data entry of the order does not matter, does not require the number of k-means algorithm to pre-specified cluster and so on. Clustering problem with obstacle constraints has very strong practical value in the spatial clustering analysis, and has become a research hotspot in recent years. Under the condition of existing obstacles constraints, the vast majority of the spatial clustering algorithm can't effectively solve the problem of irregular obstructions. Thus it has a greater impact on the accuracy of the algorithm clustering results, and reduces the efficiency of the algorithm. To solve this problem, an obstacle constraint space grid-based hierarchical clustering algorithm, which is GSHCO algorithm, is proposed. The algorithm inherits the advantages of gridbased clustering algorithm, by defining the concept of barriers to grid to deal effectively with the obstacles of arbitrary shape, to achieve the purpose of found clusters of arbitrary shape; At the same time, the algorithm uses a hierarchical strategy which can effectively reduce the complexity of the algorithm with obstacles clustering and the algorithm is improved operating efficiency. The experimental results show that the GSHCO algorithm can deal with obstacles constrained clustering, and with higher performance and better clustering quality.10aCluster ensemble10aGrid spatial clustering10aMulti-scale clustering10aSpatial cluster analysis1 aShan, D.1 aYang, Z.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84879084446&partnerID=40&md5=8173c506959188548e2182b57f3f31c201989nas a2200193   4500008004100000245008400041210006900125260000900194300001400203490000700217520131100224653002701535653002001562653002701582653004301609100001301652700001301665856011701678       2013                        eng d00aResearch on cluster analysis of high dimensional space based on fuzzy extension0 aResearch on cluster analysis of high dimensional space based on   c2013  a359 - 3650 v113 aTraditional spatial data are generally high dimensional features, and in the clustering of high dimensional data can be directly applied to data processing because of Dimension effect and the data sparseness problem. For CLIQUE algorithm, which usually have the problem such as prone to non-axis direction of over-clustering, boundary judgment of fuzzy clustering and smoothing clustering. In this paper, a fuzzy clustering algorithm based on fuzzy extension of the high-dimensional spatial data is proposed. This algorithm not only considers effect of adjacent grid of data points in the sparse grid, but also extending the sparse grid region to avoid smoothing clustering phenomenon occurs. What's more, it can alleviate the problem of over-clustering and clustering fuzzy boundaries. Firstly, this passage gives a brief introduction to the characteristics of high dimensional spatial data as well as the Clustering methods. Based on the fuzzy extension a high-dimensional space data clustering analysis algorithms is put forward. The impact of the data samples around the point on the data points within the investigated grid is considered, sparse grid fuzzy extension, and at last, the problems of clustering fuzzy boundaries and to avoid excessive clustering produce meaningless clustering are solved.10aClustering integration10aFuzzy extension10aMulti-scale clustering10aSpatial clustering of high dimensional1 aShan, D.1 aLi, W.Y.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84890448809&partnerID=40&md5=ad16385737a42e682c93f28740cee12a