Mining data records in Web pages
A large amount of information on the Web is contained in regularly structured objects, which we call data records. Such data records are important because they often present the essential information of their host pages, e.g., lists of products or services. It is useful to mine such data records in order to extract information from them to provide value-added services. Existing automatic techniques are not satisfactory because of their poor accuracies. In this paper, we propose a more effective technique to perform the task. The technique is based on two observations about data records on the Web and a string matching algorithm. The proposed technique is able to mine both contiguous and non-contiguous data records. Our experimental results show that the proposed technique outperforms existing techniques substantially.

title: Opinion Mining
abstract: Описанл процедуру формування інтегрального показника об’єкта згідно з відгуками користувачів під час застосування методів оцінювання опінії текстової інформації у web-документах. Запропоновано використання лінгвістичних змінних та застосування вагових коефіцієнтів для достовірнішого результату оцінювання емоційного забарвлення текстової інформації. Ключові слова: опінія, емоційне забарвлення, об’єкт, інтегральний показник, лінгвістична змінна, ваговий коефіцієнт.

title: Dealing with different distributions in learning from
abstract: In the problem of learning with positive and unlabeled examples, existing research all assumes that positive examples P and the hidden positive examples in the unlabeled set U are generated from the same distribution. This assumption may be violated in practice. In such cases, existing methods perform poorly. This paper proposes a novel technique A-EM to deal with the problem. Experimental results with product page classification demonstrate the effectiveness of the proposed technique.

title: Finding Actionable Knowledge via Automated Comparison
abstract: The problem of finding interesting and actionable patterns is a major challenge in data mining. It has been studied by many data mining researchers. The issue is that data mining algorithms often generate too many patterns, which make it very hard for the user to find those truly useful ones. Over the years many techniques have been proposed. However, few have made it to real-life applications. At the end of 2005, we built a data mining system for Motorola (called Opportunity Map) to enable the user to explore the space of a large number of rules in order to find actionable knowledge. The approach is based on the concept of rule cubes and operations on rule cubes. A rule cube is similar to a data cube, but stores rules. Since its deployment, some issues have also been identified during the regular use of the system in Motorola. One of the key issues is that although the operations on rule cubes are flexible, each operation is primitive and has to be initiated by the user. Finding a piece of actionable knowledge typically involves many operations and intense visual inspections, which are labor-intensive and time-consuming. From interactions with our users, we identified a generic problem that is crucial for finding actionable knowledge. The problem involves extensive comparison of sub-populations and identification of the cause of their differences. This paper first defines the problem and then proposes an effective method to solve the problem automatically. To the best of our knowledge, there is no reported study of this problem. The new method has been added to the Opportunity Map system and is now in daily use in Motorola.

title: Entity discovery and assignment for opinion mining applications
abstract: Opinion mining became an important topic of study in recent years due to its wide range of applications. There are also many companies offering opinion mining services. One problem that has not been studied so far is the assignment of entities that have been talked about in each sentence. Let us use forum discussions about products as an example to make the problem concrete. In a typical discussion post, the author may give opinions on multiple products and also compare them. The issue is how to detect what products have been talked about in each sentence. If the sentence contains the product names, they need to be identified. We call this problem entity discovery. If the product names are not explicitly mentioned in the sentence but are implied due to the use of pronouns and language conventions, we need to infer the products. We call this problem entity assignment. These problems are important because without knowing what products each sentence talks about the opinion mined from the sentence is of little use. In this paper, we study these problems and propose two effective methods to solve the problems. Entity discovery is based on pattern discovery and entity assignment is based on mining of comparative sentences. Experimental results using a large number of forum posts demonstrate the effectiveness of the technique. Our system has also been successfully tested in a commercial setting.

title: Speed-up iterative frequent itemset mining with constraint changes
abstract: Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for this purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before being satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in an enormous waste of time and computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the context of two existing frequent itemset mining algorithms, FP-tree and tree projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves a dramatic saving of computation.

title: Multi-sphere Support Vector Data Description for Outliers Detection on Multi-distribution Data
abstract: SVDD has been proved a powerful tool for outlier detection. However, in detecting outliers on multi-distribution data, namely there are distinctive distributions in the data, it is very challenging for SVDD to generate a hyper-sphere for distinguishing outliers from normal data. Even if such a hyper-sphere can be identified, its performance is usually not good enough. This paper proposes an multi-sphere SVDD approach, named MS-SVDD, for outlier detection on multi-distribution data. First, an adaptive sphere detection method is proposed to detect data distributions in the dataset. The data is partitioned in terms of the identified data distributions, and the corresponding SVDD classifiers are constructed separately. Substantial experiments on both artificial and real-world datasets have demonstrated that the proposed approach outperforms original SVDD.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: Manufacturing Grid: Needs, Concept, and Architecture
abstract: As a new approach, grid technology is rapidly used in scientific computing, large-scale data management, and collaborative work. But in the field of manufacturing, the application of grid is just at the beginning. The paper proposes the concept of manufacturing. The needs, definition and architecture of manufacturing gird are discussed, which explains why needs manufacturing grid, what is manufacturing grid and how to construct a manufacturing grid system.

title: Positive solutions of a nonlinear three-point boundary value problem
abstract: In this paper, by using Krasnoselskii's fixed point theorem in a cone, we study the existence of single and multiple positive solutions to the three-point boundary value problem (BVP)y''(t)+a(t)f(y(t))=0,0

title: Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification
abstract: In order to improve the accuracy of the conventional algorithms for multi-classifications, we propose a binary tree support vector machine based on Kernel Fisher Discriminant in this paper. To examine the training accuracy and the generalization performance of the proposed algorithm, One-against-All, One-against-One and the proposed algorithms are applied to five UCI data sets. The experimental results show that in general, the training and the testing accuracy of the proposed algorithm is the best one, and there exist no unclassifiable regions in the proposed algorithm.