Extracting Evolving Pathologies via Spectral Clustering

Incremental Spectral Clustering With Application to Monitoring of Evolving Blog Communities

In recent years, spectral clustering method has gained attentions because of its superior performance compared to other traditional clustering algorithms such as K-means algorithm. The existing spectral clustering algorithms are all off-line algorithms, i.e., they can not incrementally update the clustering result given a small change of the data set. However, the capability of incrementally up...

Spectral Clustering Using Evolving Similarity Graphs

Homework 3: Wikipedia Clustering

Clustering is an important machine learning task that tackles the problem of classifying data into distinct groups based on their features. An ideal clustering algorithm maximizes feature similarities within a cluster while minimizing the feature similarities across clusters. Some of the most common clustering algorithms include spectral clustering and k-means clustering. This project essential...

Kernel Spectral Clustering with Memory Effect

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, methabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowled...

Clustering with the Connectivity Kernel

Clustering aims at extracting hidden structure in dataset. While the problem of finding compact clusters has been widely studied in the literature, extracting arbitrarily formed elongated structures is considered a much harder problem. In this paper we present a novel clustering algorithm which tackles the problem by a two step procedure: first the data are transformed in such a way that elonga...