We present Cluster Onset Detection (COD), a novel algorithm to aid in detection of epidemic outbreaks. COD employs unsupervised learning techniques in an online setting to partition the population into subgroups, thus increasing the ability to make a detection over the population as a whole by decreasing the signal-to-noise ratio. The method is adaptive and able to alter its clustering in real-...

We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space. The algorithm is appropriate when it is necessary to cluster data efficiently as it streams in, and is to be contrasted with traditional batch clustering algorithms that have access to all data at once. For example, Links has been successfully applied to embed...

We revisit the online Unit Clustering problem in higher dimensions: Given a set of n points in R, that arrive one by one, partition the points into clusters (subsets) of diameter at most one, so as to minimize the number of clusters used. In this paper, we work in R using the L∞ norm. We show that the competitive ratio of any algorithm (deterministic or randomized) for this problem must depend ...

A general technique is proposed for embedding online clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering strategy we wish to implement. In this sense, the reinforcement guided competitive learning (RGCL) ...

In this work, a robust subspace clustering algorithm is developed to exploit the inherent union-of-subspaces structure in data for reconstructing missing measurements and detecting anomalies. Our focus on processing an incessant stream of large-scale such as synchronized phasor power grid, which challenging due computational complexity, memory requirement, corrupt observations. order mitigate t...

Topic modeling techniques have widespread use in text data mining applications. Some applications use batch models, which perform clustering on the document collection in aggregate. In this paper, we analyze and compare the performance of three recently-proposed batch topic models—Latent Dirichlet Allocation (LDA), Dirichlet Compound Multinomial (DCM) mixtures and von-Mises Fisher (vMF) mixture...

A new online clustering method called E2GK (Evidential Evolving Gustafson-Kessel) is introduced. This partitional clustering algorithm is based on the concept of credal partition defined in the theoretical framework of belief functions. A credal partition is derived online by applying an algorithm resulting from the adaptation of the Evolving Gustafson-Kessel (EGK) algorithm. Online partitionin...

To bridge the gap between the rising information needs of biological and medical researchers and the rapidly growing number of online bioinformatics resources, we have created the Online Bioinformatics Resources Collection (OBRC) at the Health Sciences Library System (HSLS) at the University of Pittsburgh. The OBRC, containing 1542 major online bioinformatics databases and software tools, was c...

The increase in social computing has provided the situation where large amounts of personal information are being posted online. This makes people vulnerable to social engineering attacks because their personal details are readily available. Our automated approach for personal data extraction was developed to extract personal details and top friends from MySpace profiles and place them into a r...