MOTIVATION: High-density SNP data of model animal resources provides opportunities for fine-resolution genetic variation studies. These genetic resources are generated through a variety of breeding schemes that involve multiple generations of matings derived from a set of founder animals. In this article, we investigate the problem of inferring the most probable ancestry of resulting genotypes, given a set of founder genotypes. Due to computational difficulty, existing methods either handle only small pedigree data or disregard the pedigree structure. However, large pedigrees of model animal resources often contain repetitive substructures that can be utilized in accelerating computation. RESULTS: We present an accurate and efficient method that can accept complex pedigrees with inbreeding in inferring genome ancestry. Inbreeding is a commonly used process in generating genetically diverse and reproducible animals. It is often carried out for many generations and can account for most of the computational complexity in real-world model animal pedigrees. Our method builds a hidden Markov model that derives the ancestry probabilities through inbreeding process without explicit modeling in every generation. The ancestry inference is accurate and fast, independent of the number of generations, for model animal resources such as the Collaborative Cross (CC). Experiments on both simulated and real CC data demonstrate that our method offers comparable accuracy to those methods that build an explicit model of the entire pedigree, but much better scalability with respect to the pedigree size.

Search engine technology plays an important role in Web information retrieval. However, with Internet information explosion, traditional searching techniques cannot provide satisfactory result due to problems such as huge number of result Web pages, unintuitive ranking, etc. Therefore, the reorganization and post-processing of Web search results have been extensively studied to help user effectively obtain useful information. Previous studies mainly focused on Web page clustering, document summary, visualization of search results, etc, which are applied separately to either text or image search. In this paper, we propose a demo to illustrate a new Web search result summary system - PictureBook, which combines text and image retrieval using techniques of multiple document summarization and image semantics analysis. Particularly, audience can interactively investigate the effect of the combined text and image summary in Web information searching and knowledge acquisition. We also introduce our new image semantic analysis method based on generalized discriminant analysis (GDA).

We investigate the problem of clustering on distributed data streams. In particular, we consider the k-median clustering on stream data arriving at distributed sites which communicate through a routing tree. Distributed clustering on high speed data streams is a challenging task due to limited communication capacity, storage space, and computing power at each site. In this paper, we propose a suite of algorithms for computing (1 + epsiv) -approximate k-median clustering over distributed data streams under three different topology settings: topology-oblivious, height-aware, and path-aware. Our algorithms reduce the maximum per node transmission topolylogN(opposed to Omega(N) for transmitting the raw data). We have simulated our algorithms on a distributed stream system with both real and synthetic datasets composed of millions of data. In practice, our algorithms are able to reduce the data transmission to a small fraction of the original data. Moreover, our results indicate that the algorithms are scalable with respect to the data volume, approximation factor, and the number of sites.

We propose an efficient algorithm for approximate biased quantile computation in large data streams. Our algorithm computes decomposable biased quantile summaries on fixed sized blocks and dynamically maintains the biased quantile summary for the entire stream as the exponential histogram over the block-wise quantile summaries. The algorithm is computationally efficient and achieves an amortized computational cost of O(log(1⁄∈log(∈n))) and a space requirement of O(log3∈n↬∈). Our algorithm does not assume prior knowledge of the stream sizes or the range of data values in the streams. In practice, our algorithm is able to efficiently maintain summaries over large data streams with over tens of millions of observations and achieves significant performance improvement over prior algorithms.

Data streams are often locally correlated, with a subset of streams exhibiting coherent patterns over a subset of time points. Subspace clustering can discover clusters of objects in different subspaces. However, traditional sub- space clustering algorithms for static data sets are not readily used for incremental clustering, and is very expensive for frequent re-clustering over dynamically changing stream data. In this paper, we present an efficient incremental sub- space clustering algorithm for multiple streams over sliding windows. Our algorithm detects all the delta-CC-Clusters, which capture the coherent changing patterns among a set of streams over a set of time points. delta-CC'-Cluster s are incrementally generated by traversing a directed acyclic graph pDAG. We propose efficient insertion and deletion operations to update the pDAG dynamically. In addition, effective pruning techniques are applied to reduce the search space. Experiments on real data sets demonstrate the performance of our algorithm.

Active learning is a promising tool to improve the performance of content-based image retrieval (CBIR). As a commonly used active learning approach, angle-diversity provides the most informative images to user for feedback. However, it suffers from the problem that the query concept is diverse and the numbers of the positive and the negative images are imbalanced. As a consequence, the positive samples obtained by active learning are inadequate, which degrades the learning efficiency. To deal with this issue, we propose a novel method based on angle-diversity and hyperplane shifting to increase the number of positive images in the active learning results. The experiment is conducted on a test data set with 10,000 images. Compared with the traditional angle-diversity technique, our method can improve the retrieval performance significantly.

Given a set of objects V with a dissimilarity measure between pairs of objects in V, a PoCluster is a collection of sets P ⊂ powerset(V) partially ordered by the ⊂ relation such that S ⊂ T iff the maximal dissimilarity among objects in S is less than the maximal dissimilarity among objects in T. PoClusters capture categorizations of objects that are not strictly hierarchical, such as those found in ontologies. PoClusters can not, in general, be constructed using hierarchical clustering algorithms. In this paper, we examine the relationship between PoClusters and dissimilarity matrices and prove that PoClusters are in one-to-one correspondence with the set of dissimilarity matrices. The PoClustering problem is NP-Complete, and we present a heuristic algorithm for it in this paper. Experiments on both synthetic and real datasets demonstrate the quality and scalability of the algorithms.

We present a fast algorithm for computing approximate quantiles in high speed data streams with deterministic error bounds. For data streams of size N where N is unknown in advance, our algorithm partitions the stream into sub-streams of exponentially increasing size as they arrive. For each sub-stream which has a fixed size, we compute and maintain a multi-level summary structure using a novel algorithm. In order to achieve high speed performance, the algorithm uses simple block-wise merge and sample operations. Overall, our algorithms for fixed-size streams and arbitrary-size streams have a computational cost of O(N log(1/epsivlogepsivN)) and an average per-element update cost of O(log logN) if epsiv is fixed.

Ontologies represent data relationships as hierarchies of possibly overlapping classes. Ontologies are closely related to clustering hierarchies, and in this article we explore this relationship in depth. In particular, we examine the space of ontologies that can be generated by pairwise dissimilarity matrices. We demonstrate that classical clustering algorithms, which take dissimilarity matrices as inputs, do not incorporate all available information. In fact, only special types of dissimilarity matrices can be exactly preserved by previous clustering methods. We model ontologies as a partially ordered set (poset) over the subset relation. In this paper, we propose a new clustering algorithm, that generates a partially ordered set of clusters from a dissimilarity matrix.