422 research outputs found
A new hierarchical clustering algorithm to identify non-overlapping like-minded communities
A network has a non-overlapping community structure if the nodes of the
network can be partitioned into disjoint sets such that each node in a set is
densely connected to other nodes inside the set and sparsely connected to the
nodes out- side it. There are many metrics to validate the efficacy of such a
structure, such as clustering coefficient, betweenness, centrality, modularity
and like-mindedness. Many methods have been proposed to optimize some of these
metrics, but none of these works well on the recently introduced metric
like-mindedness. To solve this problem, we propose a be- havioral property
based algorithm to identify communities that optimize the like-mindedness
metric and compare its performance on this metric with other behavioral data
based methodologies as well as community detection methods that rely only on
structural data. We execute these algorithms on real-life datasets of
Filmtipset and Twitter and show that our algorithm performs better than the
existing algorithms with respect to the like-mindedness metric
Kernel Spectral Clustering and applications
In this chapter we review the main literature related to kernel spectral
clustering (KSC), an approach to clustering cast within a kernel-based
optimization setting. KSC represents a least-squares support vector machine
based formulation of spectral clustering described by a weighted kernel PCA
objective. Just as in the classifier case, the binary clustering model is
expressed by a hyperplane in a high dimensional space induced by a kernel. In
addition, the multi-way clustering can be obtained by combining a set of binary
decision functions via an Error Correcting Output Codes (ECOC) encoding scheme.
Because of its model-based nature, the KSC method encompasses three main steps:
training, validation, testing. In the validation stage model selection is
performed to obtain tuning parameters, like the number of clusters present in
the data. This is a major advantage compared to classical spectral clustering
where the determination of the clustering parameters is unclear and relies on
heuristics. Once a KSC model is trained on a small subset of the entire data,
it is able to generalize well to unseen test points. Beyond the basic
formulation, sparse KSC algorithms based on the Incomplete Cholesky
Decomposition (ICD) and , , Group Lasso regularization are
reviewed. In that respect, we show how it is possible to handle large scale
data. Also, two possible ways to perform hierarchical clustering and a soft
clustering method are presented. Finally, real-world applications such as image
segmentation, power load time-series clustering, document clustering and big
data learning are considered.Comment: chapter contribution to the book "Unsupervised Learning Algorithms
Meta Clustering
Clustering is ill-defined. Unlike supervised learning where labels
lead to crisp performance criteria such as accuracy and squared error, clustering quality depends on how the clusters will be used. Devising clustering criteria that capture what users need is difficult. Most clustering algorithms search for one optimal clustering based on a pre-specified clustering criterion. Once that clustering has been determined, no further clusterings are examined. Our approach differs in that we search for many alternate reasonable clusterings of the data, and then allow users to select the clustering(s) that best fit their needs. Any reasonable partitioning of the data is potentially useful for some purpose, regardless of whether or not it is optimal according to a specific clustering criterion. Our approach first finds a variety of reasonable clusterings. It then clusters this diverse set of clusterings so that users must only examine a small number of qualitatively different clusterings. In this paper, we present methods for automatically generating a diverse set of alternate clusterings, as well as methods for grouping clusterings into meta clusters. We evaluate meta clustering on four test problems, and then apply meta clustering to two case studies. Surprisingly, clusterings that would be of most interest to users often are not very compact clusterings
AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number
<p>Abstract</p> <p>Background</p> <p>Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry.</p> <p>Results</p> <p>We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four.</p> <p>Conclusions</p> <p>By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at <url>http://jimcooperlab.mcdb.ucsb.edu/autosome</url>.</p
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