The problem of measuring similarity between web pages arises in many important Web applications, such as search engines and Web directories. In this paper, we propose a novel neighbor-based similarity measure called MatchSim, which uses only the neighborhood structure of web pages. Technically, MatchSim recursively defines similarity between web pages by the average similarity of the maximum matching between their neighbors. Our method extends the traditional methods which simply count the numbers of common and/or different neighbors. It also successfully overcomes a severe counterintuitive loophole in SimRank, due to its strict consistency with the intuitions of similarity. We give the computational complexity of MatchSim iteration. The accuracy of MatchSim is compared against others on two real datasets. The results show that our method performs best in most cases.

To efficiently and effectively mine massive amounts of data in the time series, approximate representation of the data is one of the most commonly used strategies. Piecewise Linear Approximation is such an approach, which represents a time series by dividing it into segments and approximating each segment with a straight line. In this paper, we first propose a new segmentation criterion that improves computing efficiency. Based on this criterion, two novel online piecewise linear segmentation methods are developed, the feasible space window method and the stepwise feasible space window method. The former usually produces much fewer segments and is faster and more reliable in the running time than other methods. The latter can reduce the representation error with fewer segments. It achieves the best overall performance on the segmentation results compared with other methods. Extensive experiments on a variety of real-world time series have been conducted to demonstrate the advantages of our methods.

The problem of fnding similar pages to a given web page arises in many web applications such as search engine. In this paper, we focus on the link-based similarity measures which compute web page similarity solely from the hyperlinks of the Web. We first propose a simple model called the Extended Neighborhood Structure (ENS), which defines a bi-directional (in-link and out-link) and multi-hop neighborhood structure. Based on the ENS model, several existing similarity measures are extended. Preliminary experimental results show that the accuracy of the extended algorithms are signifcantly improved.

The requirement for measuring the similarity between Web pages arises in many applications on the Web, such as Web searching engine and Web document classification. According to the unique characteristics of the Web, which are huge, rapidly growing, high dynamic, and untrustworthy, we propose a novel link-based similarity measure called PageSim. Based on the strategy of PageRank score propagation, PageSim is efficient, scalable, stable, and "fairly" robust, and therefore is applicable to the Web. We present intuitions behind the PageSim model, and outline the model with mathematical definitions. We also suggest the pruning technique for efficient computation of PageSim scores, and conduct experiments to illustrate the effectiveness and specialities of PageSim

To find similar web pages to a query page on the Web, this paper introduces a novel link-based similarity measure, called PageSim. Contrast to SimRank, a recursive refinement of cocitation, PageSim can measure similarity between any two web pages, whereas SimRank cannot in some cases. We give some intuitions to the PageSim model, and outline the model with mathematical definitions. Finally, we give an example to illustrate its effectiveness.