COOPERATIVE CLUSTERING BASED ON GRID AND DENSITY

Based on the analysis of features of the grid-based clustering method-clustering in quest （CLIQUE） and density-based clustering method-density-based spatial clustering of applications with noise （DBSCAN）, a new clustering algorithm named cooperative clustering based on grid and density （CLGRID） is presented. The new algorithm adopts an equivalent rule of regional inquiry and density unit identification. The central region of one class is calculated by the grid-based method and the margin region by a density-based method. By clustering in two phases and using only a small number of seed objects in representative units to expand the cluster, the frequency of region query can be decreased, and consequently the cost of time is reduced. The new algorithm retains positive features of both grid-based and density-based methods and avoids the difficulty of parameter searching. It can discover clusters of arbitrary shape with high efficiency and is not sensitive to noise. The application of CLGRID on test data sets demonstrates its validity and higher efficiency, which contrast with tradi- tional DBSCAN with R tree.

An Algorithm for Mining Gradual Moving Object Clusters Pattern From Trajectory Streams

The discovery of gradual moving object clusters pattern from trajectory streams allows characterizing movement behavior in real time environment,which leverages new applications and services.Since the trajectory streams is rapidly evolving,continuously created and cannot be stored indefinitely in memory,the existing approaches designed on static trajectory datasets are not suitable for discovering gradual moving object clusters pattern from trajectory streams.This paper proposes a novel algorithm of gradual moving object clusters pattern discovery from trajectory streams using sliding window models.By processing the trajectory data in current window,the mining algorithm can capture the trend and evolution of moving object clusters pattern.Firstly,the density peaks clustering algorithm is exploited to identify clusters of different snapshots.The stable relationship between relatively few moving objects is used to improve the clustering efficiency.Then,by intersecting clusters from different snapshots,the gradual moving object clusters pattern is updated.The relationship of clusters between adjacent snapshots and the gradual property are utilized to accelerate updating process.Finally,experiment results on two real datasets demonstrate that our algorithm is effective and efficient.

Automatic Video Segmentation Algorithm by Background Model and Color Clustering

In order to detect the object in video efficiently, an automatic and real time video segmentation algorithm based on background model and color clustering is proposed. This algorithm consists of four phases: background restoration, moving objects extract, moving objects region clustering and post processing. The threshold of the background restoration is not given in advanced. It can be gotten automatically. And a new object region cluster algorithm based on background model and color clustering to remove significance noise is proposed. An efficient method of eliminating shadow is also used. This approach was compared with other methods on pixel error ratio. The experiment result indicates the algorithm is correct and efficient.

Realization of R-tree for GIS on hybrid clustering algorithm

The characteristic of geographic information system（GfS） spatial data operation is that query is much more frequent than insertion and deletion, and a new hybrid spatial clustering method used to build R-tree for GIS spatial data was proposed in this paper. According to the aggregation of clustering method, R-tree was used to construct rules and specialty of spatial data. HCR-tree was the R-tree built with HCR algorithm. To test the efficiency of HCR algorithm, it was applied not only to the data organization of static R-tree but also to the nodes splitting of dynamic R-tree. The results show that R-tree with HCR has some advantages such as higher searching efficiency, less disk accesses and so on.

REPETITIVE CLUSTER-TILTED ALGEBRAS

Let H be a finite-dimensional hereditary algebra over an algebraically closed field k and CFm be the repetitive cluster category of H with m ≥ 1. We investigate the properties of cluster tilting objects in CFm and the structure of repetitive clustertilted algebras. Moreover, we generalize Theorem 4.2 in [12] （Buan A, Marsh R, Reiten I. Cluster-tilted algebra, Trans. Amer. Math. Soc. 359（1）（2007）, 323-332.） to the situation of C Fm, and prove that the tilting graph KCFm of CFm is connected.

The Dimension Vectors of Indecomposable Modules of Cluster-tilted Algebras and the Fomin-Zelevinsky Denominators Conjecture

The main result of this paper is that any two non-isomorphic indecomposable modules of a cluster-tilted algebra of finite representation type have different dimension vectors. As an application to cluster algebras of Types A, D, E, we give a proof of the Fomin Zelevinsky denominators conjecture for cluster variables, namely, different cluster variables have different denominators with respect to any given cluster.