Clustering Method Using Multidimensional Address Sorting

Top-down Clustering Using Multidimensional Indexes

Clustering on large databases has been studied actively as an increasing number of applications involve huge amount of data. In this paper, we propose a novel top-down clustering method based on region density using a multidimensional index. Generally, multidimensional indexes have the clustering property of storing similar objects in the same or adjacent data pages. By taking advantage of this...

A New Clustering Method for Landsat Images Using Local Maximums of a Multidimensional Histogram

Unsupervised multidimensional hierarchical clustering

A method for multidimensional hierarchical clustering that is invariant to monotonic transformations of the distance metric is presented. The method derives a tree of clusters organized according to the homogeneity of intracluster and interpoint distances. Higher levels correspond to coarser clusters. At any level the method can detect clusters of different densities, shapes and sizes. The numb...

A spike sorting framework using nonparametric detection and incremental clustering

We introduce a statistical computing framework to address two important issues in spike sorting: flexible spike shape modeling and realtime spike clustering. In this framework, spikes are detected based on a nonparametric shape distribution; detected spikes are further grouped by an incremental clustering algorithm involving the second-order statistics–covariance matrix. We performed experiment...

Fuzzy Clustering Using Kernel Method

Classical fuzzy C -means (FCM) clustering is performed in the input space, given the desired number of clusters. Although it has proven effective for spherical data, it fails when the data structure of input patterns is non-spherical and complex. In this paper, we present a novel kernel-based fuzzy C-means clustering algorithm (KFCM). Its basic idea is to transform implicitly the input data int...