Image Cluster Method Based on Ensemble Locality Sensitive Clustering

A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble

An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...

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 ...

Time Series Clustering Ensemble Algorithm Based on Locality Preserving Projection

The time series clustering is one of the important research contents in the time series data mining. Since the dimension of time series is common high, the performance of direct raw time series data clustering is not ideal. How to improve the clustering performance of time series is the main research point of this paper. Firstly, use Locality Preserving Projection (LPP) to time series samples f...

Locality Sensitive K-means Clustering

A cluster ensemble method for clustering categorical data

Categorical data clustering (CDC) and cluster ensemble (CE) have long been considered as separate research and application areas. The main focus of this paper is to investigate the commonalities between these two problems and the uses of these commonalities for the creation of new clustering algorithms for categorical data based on cross-fertilization between the two disjoint research fields. M...