Housing markets play a crucial role in economies and the collapse of a real-estate bubble usually destabilizes the financial system and causes economic recessions. We investigate the systemic risk and spatiotemporal dynamics of the US housing market (1975-2011) at the state level based on the Random Matrix Theory (RMT). We identify richer economic information in the largest eigenvalues deviatin...

This paper proposes a new distance metric between clusterings that incorporates information about the spatial distribution of points and clusters. Our approach builds on the idea of a Hilbert space-based representation of clusters as a combination of the representations of their constituent points. We use this representation and the underlying metric to design a spatially-aware consensus cluste...

Cluster ensembles generate a large number of different clustering solutions and combine them into a more robust and accurate consensus clustering. On forming the ensembles, the literature has suggested that higher diversity among ensemble members produces higher performance gain. In contrast, some studies also indicated that medium diversity leads to the best performing ensembles. Such contradi...

This paper investigates the application of consensus clustering and meta-clustering to the set of all possible partitions of a data set. We show that when using a ”complement” of Rand Index as a measure of cluster similarity, the total-separation partition, putting each element in a separate set, is chosen.

Recently ensemble selection for consensus clustering has emerged as a research problem in Machine Intelligence. Normally consensus clustering algorithms take into account the entire ensemble of clustering, where there is a tendency of generating a very large size ensemble before computing its consensus. One can avoid considering the entire ensemble and can judiciously select few partitions in t...

The clustering method described in this paper is meant to aid the researcher who, for any of a variety of reasons, has clustered a data set a large number of times and is now faced with the problem of reconciling many different clustering results into a single, robust clustering solution. In this paper a clustering method is developed that takes the results of these many data clusterings, prope...

Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could diffe...

This chapter describes the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determine these partitionings – popularly known as the problem of “consensus clustering”. We illustrate different algorithms for solving the consensus clustering problem. The notion of dissimilarity between a pair of c...