A Join Index is a data structure that optimizes the join query processing in spatial databases. Join indices use pre-computation techniques to speed up online query processing and are useful for applications which require low update rates. The cost of spatial join computation using a join-index with limited buuer space depends primarily on the page access sequence used to fetch the pages of the...

Trajectory clustering has played a crucial role in data analysis since it reveals underlying trends of moving objects. Due to their sequential nature, trajectory data are often received incrementally, e.g., continuous new points reported by GPS system. However, since existing trajectory clustering algorithms are developed for static datasets, they are not suitable for incremental clustering wit...

XML clustering finds many applications, ranging from storage to query processing. However, existing clustering algorithms focus on static XML collections, whereas modern information systems frequently deal with streaming XML data that needs to be processed online. Streaming XML clustering is a challenging task because of the high computational and space efficiency requirements implicated for on...

In the context of graph clustering, we consider the problem of estimating simultaneously both the partition of the graph nodes and the parameters of an underlying mixture of affiliation networks. In numerous applications the rapid increase of data size with time makes classical clustering algorithms too slow because of the high computational cost. In such situations online clustering algorithms...

Most existing traditional grid-based clustering algorithms for uncertain data streams that used the fixed meshing method have the disadvantage of low clustering accuracy. In view of above deficiencies, this paper proposes a novel algorithm APDG-CUStream, Adjustable Probability Density Grid-based Clustering for Uncertain Data Streams, which adopts the online component and offline component. In o...

Due to the limited number of labeled samples, semisupervised learning often leads a considerable empirical distribution mismatch between samples and unlabeled samples. To this end, paper proposes novel algorithm named Local Gravitation-based Semisupervised Online Sequential Extreme Learning Machine (LGS-OSELM), follows from easy difficult. Each sample is formulated as an object with mass associ...