32299699
Efficient PageRank with Same Out-Link Groups
Traditional PageRank algorithm suffers from heavy computation cost due to the huge number of web pages. In this paper, we propose a more efficient algorithm to compute the pagerank value for each web page directly on the same out-link groups. This new algorithm groups the pages with the same out-link behavior (SOLB) as a unit. It is proved that the derived PageRank is the same as that from the original PageRank algorithm which calculates over single webpage; while our proposed algorithm improve the efficiency greatly. For simplicity, we restrict the group within a directory and define metrics to measure the similarity of the pages in same out-link behavior. We design the experiments to group from 0.5 liked to exact SOLB pages; the results show that such group offers similar rank scores as traditional PageRank algorithm does and achieves a remarkable 50% on efficiency.
16533233
Improving text classification using local latent semantic indexing
Latent semantic indexing (LSI) has been shown to be extremely useful in information retrieval, but it is not an optimal representation for text classification. It always drops the text classification performance when being applied to the whole training set (global LSI) because this completely unsupervised method ignores class discrimination while only concentrating on representation. Some local LSI methods have been proposed to improve the classification by utilizing class discrimination information. However, their performance improvements over original term vectors are still very limited. In this paper, we propose a new local LSI method called "local relevancy weighted LSI" to improve text classification by performing a separate single value decomposition (SVD) on the transformed local region of each class. Experimental results show that our method is much better than global LSI and traditional local LSI methods on classification within a much smaller LSI dimension.
10032259
Effective and efficient dimensionality reduction for large-scale and streaming data preprocessing
Dimensionality reduction is an essential data preprocessing technique for large-scale and streaming data classification tasks. It can be used to improve both the efficiency and the effectiveness of classifiers. Traditional dimensionality reduction approaches fall into two categories: feature extraction and feature selection. Techniques in the feature extraction category are typically more effective than those in feature selection category. However, they may break down when processing large-scale data sets or data streams due to their high computational complexities. Similarly, the solutions provided by the feature selection approaches are mostly solved by greedy strategies and, hence, are not ensured to be optimal according to optimized criteria. In this paper, we give an overview of the popularly used feature extraction and selection algorithms under a unified framework. Moreover, we propose two novel dimensionality reduction algorithms based on the orthogonal centroid algorithm (OC). The first is an incremental OC (IOC) algorithm for feature extraction. The second algorithm is an orthogonal centroid feature selection (OCFS) method which can provide optimal solutions according to the OC criterion. Both are designed under the same optimization criterion. Experiments on Reuters Corpus Volume-1 data set and some public large-scale text data sets indicate that the two algorithms are favorable in terms of their effectiveness and efficiency when compared with other state-of-the-art algorithms.
1876424
A Similarity Reinforcement Algorithm for Heterogeneous Web Pages
Many machine learning and data mining algorithms crucially rely on the similarity metrics. However, most early research works such as Vector Space Model or Latent Semantic Index only used single relationship to measure the similarity of data objects. In this paper, we first use an Intra- and Inter- Type Relationship Matrix (IITRM) to represent a set of heterogeneous data objects and their inter-relationships. Then, we propose a novel similarity-calculating algorithm over the Inter- and Intra- Type Relationship Matrix. It tries to integrate information from heterogeneous sources to serve their purposes by iteratively computing. This algorithm can help detect latent relationships among heterogeneous data objects. Our new algorithm is based on the intuition that the intra-relationship should affect the inter-relationship, and vice versa. Experimental results on the MSN logs dataset show that our algorithm outperforms the traditional Cosine similarity.
61769105
Similarity of Temporal Query Logs Based on ARIMA Model
A challenging issue faced by modern information retrieval is that of determining and satisfying users? requirements relying only on very short text queries. In this paper, we propose an algorithm to find out related queries based on Auto-Regressive Integrated Moving Average (ARIMA) Model. First, we select and estimate ARIMA model of the temporal query logs. And then each query is denoted by a sequence of coefficients. We use the correlation of ARIMA coefficients as the similarity measurement. We call it as the ARIMA Temporal Similarity (ARIMA TS). This similarity describes how strongly two time series are linearly related. On the other hand, the ARIMA model could also be treated as a dimensionality reduction procedure. It can save storage space for a large database of the query logs. In addition, ARIMA model could be used as a tool to predict the trend of a query. The experimental results on two query logs of MSN search engine 1 demonstrate that the proposed approach can achieve better similarity measurement efficiently.
15657776
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Web link analysis has proven to be a significant enhancement for quality based web search. Most existing links can be classified into two categories: intra-type links (e.g., web hyperlinks), which represent the relationship of data objects within a homogeneous data type (web pages), and inter-type links (e.g., user browsing log) which represent the relationship of data objects across different data types (users and web pages). Unfortunately, most link analysis research only considers one type of link. In this paper, we propose a unified link analysis framework, called "link fusion", which considers both the inter- and intra- type link structure among multiple-type inter-related data objects and brings order to objects in each data type at the same time. The PageRank and HITS algorithms are shown to be special cases of our unified link analysis framework. Experiments on an instantiation of the framework that makes use of the user data and web pages extracted from a proxy log show that our proposed algorithm could improve the search effectiveness over the HITS and DirectHit algorithms by 24.6% and 38.2% respectively.
10593169
ProMiR II: a web server for the probabilistic prediction of clustered, nonclustered, conserved and nonconserved microRNAs
ProMiR is a web-based service for the prediction of potential microRNAs (miRNAs) in a query sequence of 60–150 nt, using a probabilistic colearning model. Identification of miRNAs requires a computational method to predict clustered and nonclustered, conserved and nonconserved miRNAs in various species. Here we present an improved version of ProMiR for identifying new clusters near known or unknown miRNAs. This new version, ProMiR II, integrates additional evidence, such as free energy data, G/C ratio, conservation score and entropy of candidate sequences, for more controllable prediction of miRNAs in mouse and human genomes. It also provides a wider range of services, e.g. the prediction of miRNA genes in long nonrelated sequences such as viral genomes. Importantly, we have validated this method using several case studies. All data used in ProMiR II are structured in the MySQL database for efficient analysis. The ProMiR II web server is available at .
44569612
A New Cache Replacement Algorithm in SMO
In the methods for training Support Vector Machines (SVM), precomputed elements in the Hessian matrix are usually cached in order to avoid recomputation. However, the least-recent-used replacement algorithm that is widely used is not suitable since the elements are requested in an irregular way. A new cache replacement algorithm applied in Sequential Minimal Optimization (SMO) is presented in the paper. The item corresponding to the component with minimal violation of the Karush-Kuhn-Tucher (KKT) condition is deleted to make room for new one when the cache is full. It is shown in the experiments that the hit ratio of the cache is improved compared with LRU cache while the training time can be reduced in the tasks where the computation of elements in Hessian matrix is very time-consuming.
41094552
The Statistical Inference Method in Heuristic Search Techniques
In this paper we present a new heuristic searching algorithm by introducing statistical inference method on the basic of algorithm A*. It's called algorithm SA*. The following results have been proved. (1) Algorithm SA* in superior to algorithm A*. (2) The mean complexity of SA* is CN2, but in some case A* exhibits exponential complexity (eN). (3) In a (N,d,F)- game tree, the mean complexity of SA* is CN2, but the complexity of other known game-searching algoritham (α-β , SSS* etc.) is at least dN. (4) The maximal storage-space required by SA* is C,N. 
 
This shows that under a given significance level SA* is superior to other known algorithm (e.g. A*, B*, α-β, SSS* etc.).
6883417
Fast k-nearest neighbor classification using cluster-based trees
Most fast k-nearest neighbor (k-NN) algorithms exploit metric properties of distance measures for reducing computation cost and a few can work effectively on both metric and nonmetric measures. We propose a cluster-based tree algorithm to accelerate k-NN classification without any presuppositions about the metric form and properties of a dissimilarity measure. A mechanism of early decision making and minimal side-operations for choosing searching paths largely contribute to the efficiency of the algorithm. The algorithm is evaluated through extensive experiments over standard NIST and MNIST databases.
14591402
An analysis of convergence delay in path vector routing protocols
Path vector routing protocols such as the Border Gateway Protocol (BGP) are known to suffer from slow convergence following a change in the network topology or policy. Although a number of convergence enhancements have been proposed recently, there has been no general analytical framework to assess and compare the various proposed algorithms. In this paper we present such a general framework to analyze the upper bounds of path vector protocols' convergence delay under shortest path routing policy and single link failure. Our framework takes into account important factors such as network connectivity, failure location, and routing message processing delay. It can be used to analyze both standard BGP and all the proposed convergence improvement algorithms in the case of shortest path routing policy and single link failure. It enables us to obtain previously unavailable analytical results, including the delay bounds of path fail-over for standard BGP and its convergence enhancements. Our analysis shows that BGP fail-over delay bounds are mainly determined by two factors: (1) the distance between the failure location and the destination, and (2) the length of the longest alternate path to reach the destination after the failure. These two factors are captured formally by our analysis and can explain why existing convergence enhancements often provide only limited improvements in fail-over events. Moreover, explicitly modeling message processing delay reveals insights into the impacts of connectivity richness (i.e., node degree and total number of links in the network), and also the effectiveness of different enhancements. These new results enable one to better understand and compare the behavior of various path vector protocols under different topology structures, network sizes, and message delays.