Cluster analysis is widely applied in the neuropsychological field for exploring patterns cognitive profiles, but traditional hierarchical and non-hierarchical approaches could be often poorly effective or even inapplicable on certain type of data. Moreover, these need initial specification number clusters, based a priori knowledge not always owned. For this reason, we proposed novel method clu...

This report describes the methods that our Information Retrieval Group at Purdue University used for the TREC Microblog 2011 track. The first method is the pseudo-relevance feedback, a traditional algorithm to reformulate the query by adding expanded terms to the query. The second method is the affinity propagation, a non parametric clustering algorithm that can group the top tweets according t...

Original scientific paper In view of the unsatisfying clustering effect of affinity propagation (AP) clustering algorithm when dealing with data sets of complex structures, a semi-supervised affinity propagation clustering algorithm based on density peaks (SAP-DP) was proposed in this paper. The algorithm uses a new algorithm of density peaks (DP) which has the advantage of the manifold cluster...

We consider the problem of reducing a potentially very large dataset to a subset of representative prototypes. Rather than searching over the entire space of prototypes, we first roughly divide the data into balanced clusters using bisecting k-means and spectral cuts, and then find the prototypes for each cluster by affinity propagation. We apply our algorithm to text data, where we perform an ...

A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper...