In this paper, we present a kernel sparse subspace clustering with spatial max pooling operation (KSSC-SMP) algorithm for hyperspectral remote sensing imagery. Firstly, the feature points are mapped from the original space into a higher dimensional space with a kernel strategy. In particular, the sparse subspace clustering (SSC) model is extended to nonlinear manifolds, which can better explore...

version on a funder's repository at a funder's request, provided it is not made publicly available until 12 months after publication. Co-clustering has been broadly applied to many domains such as bioinformatics and text mining. However, model-based spatial co-clustering has not been studied. In this paper, we develop a co-clustering method using a generalized linear mixed model for spatial dat...

We propose a cost-based query-adaptive clustering solution for multidimen-sional objects with spatial extents to speed-up execution of spatial range queries (e.g.,intersection, containment). Our work was motivated by the emergence of many SDIapplications (Selective Dissemination of Information) bringing out new real challengesfor the multidimensional data indexing. Our clusterin...

Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering...

Fuzzy C-means is a widely used clustering algorithm in data mining. Since traditional fuzzy C-means algorithms do not take spatial information into consideration, they often can’t effectively explore geographical data information. So in this paper, we design a Spatial Distance Weighted Fuzzy C-Means algorithm, named as SDWFCM, to deal with this problem. This algorithm can fully use spatial feat...

With the rapid explosion of information based on location, spatial clustering plays an increasingly significant role in this day and age as an important technique in geographical data analysis. Most existing spatial clustering algorithms are limited by complicated spatial patterns, which have difficulty in discovering clusters with arbitrary shapes and uneven density. In order to overcome such ...

Neighboring neurons in cat primary visual cortex (V1) have similar preferred orientation, direction, and spatial frequency. How diverse is their degree of tuning for these properties? To address this, we used single-tetrode recordings to simultaneously isolate multiple cells at single recording sites and record their responses to flashed and drifting gratings of multiple orientations, spatial f...

Clustering is a fundamental task in Spatial Data Mining where data consists of observations for a site (e.g. areal units) descriptive of one or more (spatial) primary units, possibly of different type, collected within the same site boundary. The goal is to group structured objects, i.e. data collected at different sites, such that data inside each cluster models the continuity of socio-economi...