Mining interesting patterns from transaction databases has attracted a lot of research interest for more than a decade. Most of those studies use frequency, the number of times a pattern appears in a transaction database, as the key measure for pattern interestingness. In this paper, we introduce a new measure of pattern interestingness, occupancy. The measure of occupancy is motivated by some realworld pattern recommendation applications which require that any interesting pattern X should occupy a large portion of the transactions it appears in. Namely, for any supporting transaction t of pattern X, the number of items in X should be close to the total number of items in t. In these pattern recommendation applications, patterns with higher occupancy may lead to higher recall while patterns with higher frequency lead to higher precision. With the definition of occupancy we call a pattern dominant if its occupancy is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both frequent and dominant. Additionally, we also formulate the problem of mining top-k qualified patterns: finding the qualified patterns with the top-k values of any function (e.g. weighted sum of both occupancy and support).The challenge to these tasks is that the monotone or anti-monotone property does not hold on occupancy. In other words, the value of occupancy does not increase or decrease monotonically when we add more items to a given itemset. Thus, we propose an algorithm called DOFIA (DOminant and Frequent Itemset mining Algorithm), which explores the upper bound properties on occupancy to reduce the search process. The tradeoff between bound tightness and computational complexity is also systematically addressed. Finally, we show the effectiveness of DOFIA in a real-world application on print-area recommendation for Web pages, and also demonstrate the efficiency of DOFIA on several large synthetic data sets.

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Frequent pattern mining is an important data mining problem with many broad applications. Most studies in this field use support (frequency) to measure the popularity of a pattern, namely the fraction of transactions or sequences that include the pattern in a data set. In this study, we introduce a new interesting measure, namely occupancy, to measure the completeness of a pattern in its supporting transactions or sequences. This is motivated by some real-world pattern recommendation applications in which an interesting pattern should not only be frequent, but also occupies a large portion of its supporting transactions or sequences. With the definition of occupancy we call a pattern dominant if its occupancy value is above a user-specified threshold. Then, our task is to identify the qualified patterns which are both dominant and frequent. Also, we formulate the problem of mining top-k qualified patterns , that is, finding k qualified patterns with maximum values on a user-defined function of support and occupancy, for example, weighted sum of support and occupancy. The challenge to these tasks is that the value of occupancy does not change monotonically when more items are appended to a given pattern. Therefore, we propose a general algorithm called DOFRA (DOminant and FRequent pattern mining Algorithm) for mining these qualified patterns, which explores the upper bound properties on occupancy to drastically reduce the search process. Finally, we show the effectiveness of DOFRA in two real-world applications and also demonstrate the efficiency of DOFRA on several real and large synthetic datasets.

Clipping Web pages, namely extracting the informative clips (areas) from Web pages, has many applications, such as Web printing and e-reading on small handheld devices. Although many existing methods attempt to address this task, most of them can either work only on certain types of Web pages (e.g., news-and bloglike web pages), or perform semi-automatically where extra user efforts are required in adjusting the outputs. The problem of clipping any types of Web pages accurately in a totally automatic way remains pretty much open. To this end in this study we harness the wisdom of the crowds to provide accurate recommendation of informative clips on any given Web pages. Specifically, we leverage the knowledge on how previous users clip similar Web pages, and this knowledge repository can be represented as a transaction database where each transaction contains the clips selected by a user on a certain Web page. Then, we formulate a new pattern mining problem, mining top-1 qualified pattern, on transaction database for this recommendation. Here, the recommendation considers not only the pattern support but also the pattern occupancy (proposed in this work). High support requires that patterns appear frequently in the database, while high occupancy requires that patterns occupy a large portion of the transactions they appear in. Thus, it leads to both precise and complete recommendation. Additionally, we explore the properties on occupancy to further prune the search space for high-efficient pattern mining. Finally, we show the effectiveness of the proposed algorithm on a human-labeled ground truth dataset consisting of 2000 web pages from 100 major Web sites, and demonstrate its efficiency on large synthetic datasets.

Abstract. Network Coordinate System (NCS) is an efficient and scalable mechanism to predict latency between any two network hosts based on historical measurements. Most NCS models, such as metric space embedding based, like Vivaldi, and matrix factorization based, like DMF and Phoenix, use squared error measure in training which suffers from the erroneous records, i.e. the records with large noise. To overcome this drawback, we introduce an elegant error measure, the Huber norm to network latency prediction. The Huber norm shows its robustness to the large data noise while remaining efficiency of optimization. Based on that, we upgrade the traditional NCS models into more robust versions, namely Robust Vivaldi model and Robust Matrix Factorization model. We conduct extensive experiments to compare the proposed models with traditional ones and the results show that our approaches significantly increase the accuracy of network latency prediction.