Enhanced Locality Sensitive Clustering in High Dimensional Space

Locality Sensitive Clustering in High Dimensional Space

In high dimension space, many conventional clustering algorithms do not work well in effectiveness and efficiency, especially for image data set. For example, k-means is widely used in image clustering especially visual clustering. But its drawback such as long clustering time and high memory cost seriously deteriorates feasibility in incremental large image set. To improve the feasibility, we ...

Locality Sensitive K-means Clustering

Locality Sensitive Hashing Based Clustering

Definition In learning systems with kernels, the shape and size of a kernel plays a critical role for accuracy and generalization. Most kernels have a distance metric parameter, which determines the size and shape of the kernel in the sense of a Mahalanobis distance. Advanced kernel learning tune every kernel’s distance metric individually, instead of turning one global distance metric for all ...

Chromatic Clustering in High Dimensional Space

In this paper, we study a new type of clustering problem, called Chromatic Clustering, in high dimensional space. Chromatic clustering seeks to partition a set of colored points into groups (or clusters) so that no group contains points with the same color and a certain objective function is optimized. In this paper, we consider two variants of the problem, chromatic k-means clustering (denoted...

Flexible Clustering by Tendency in High Dimensional Space

Clustering is the process of grouping a set of objects into classes of similar objects. Until recently, the concept of similarity is based on distances, e.g Euclidean distance and cosine distance. Our previous work on δ-cluster and δ-pCluster designed new similarity models to capture subspace coherency exhibited in data and focused on shifting patterns or scaling patterns. Along the same genera...