Analyzing the interestingness of association rules from the temporal dimension
Rule discovery is one of the central tasks of data mining. Existing research has produced many algorithms for the purpose. These algorithms, however, often generate too many rules. In the past few years, rule interestingness techniques were proposed to help the user find interesting rules. These techniques typically employ the dataset as a whole to mine rules, and then filter and/or rank the discovered rules in various ways. We argue that this is insufficient. These techniques are unable to answer a question that is of critical importance to the application of rules, i.e., can the rules be trusted? In practice, the users are always concerned with the question. They want to know whether the rules indeed represent some true and stable (or reliable) underlying relationships in the domain. If a rule is not stable, does it show any systematic pattern such as a trend? Before any rule can be used, these questions must be answered. The paper proposes a technique to use statistical methods to analyze rules from the temporal dimension to answer these questions. Experimental results show that the proposed technique is very effective.

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: 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: 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: 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: 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: Theoretical study on the Br + CH3SCH3 reaction
abstract: The multiple-channel reactions Br + CH(3)SCH(3) --> products are investigated by direct dynamics method. The optimized geometries, frequencies, and minimum energy path are all obtained at the MP2/6-31+G(d,p) level, and energetic information is further refined by the G3(MP2) (single-point) theory. The rate constants for every reaction channels, Br + CH(3)SCH(3) --> CH(3)SCH(2) + HBr (R1), Br + CH(3)SCH(3) --> CH(3)SBr + CH(3) (R2), and Br + CH(3)SCH(3) -->CH(3)S + CH(3)Br (R3), are calculated by canonical variational transition state theory with small-curvature tunneling correction over the temperature range 200-3000 K. The total rate constants are in good agreement with the available experimental data, and the two-parameter expression k(T) = 2.68 x 10(-12) exp(-1235.24/T) cm(3)/(molecule s) over the temperature range 200-3000 K is given. Our calculations indicate that hydrogen abstraction channel is the major channel due to the smallest barrier height among three channels considered, and the other two channels to yield CH(3)SBr + CH(3) and CH(3)S + CH(3)Br are minor channels over the whole temperature range.

title: Multi-Space-Mapped SVMs for Multi-class Classification
abstract: In SVMs-based multiple classification, it is not always possible to find an appropriate kernel function to map all the classes from different distribution functions into a feature space where they are linearly separable from each other. This is even worse if the number of classes is very large. As a result, the classification accuracy is not as good as expected. In order to improve the performance of SVMs-based multi-classifiers, this paper proposes a method, named multi-space-mapped SVMs, to map the classes into different feature spaces and then classify them. The proposed method reduces the requirements for the kernel function. Substantial experiments have been conducted on one-against-all, one-against-one, FSVM, DDAG algorithms and our algorithm using six UCI data sets. The statistical results show that the proposed method has a higher probability of finding appropriate kernel functions than traditional methods and outperforms others.

title: A High-Performance Intelligent Integrated Data Services System in Data Grid
abstract: It is very complex and of low efficiency for grid users to access heterogeneous diverse data resources distributed over the whole wide area network. The integrated data services can implement more wide range of transparent access to data resources in Data Grid. One of the important building blocks for any Data Grid is the server which implements data services. In this paper, we describe HPIIDS which implements integrated data services and achieves more wide range of transparencies. In order to improve its performances, we emphasis on the server called DSB which provides integrated data services. The DSB bases on cluster and agent technologies. Agent-based DSB can cleverly prefetch required data, replicate them among DSBs. Multiple DSBs are formed cluster HPIIDS. Cluster-based HPIIDS implements a single entry, provides a virtual integrated storage system for users, hides distributed heterogeneous low-level data resources, and insures load balance of each server. HPIIDS architecture supporting these various scenarios, are also described.

title: A note on a nonlocal boundary value problems
abstract: A nonlocal boundary value problems for a second-order ordinary differential equation is considered in this note. An existence result is obtained by the help of the coincidence degree theory.

title: Accelerated Bundle Adjustment in Multiple-View Reconstruction
abstract: Bundle adjustment is one of the main tools used for multi- ple view reconstruction. It seeks to refine the estimate of the 3D scene and the view parameter that minimize a certain cost function, e.g. the overall reprojection error. In this paper we propose a new algorithm to simplify the computation of the reprojection error for multiple views. With this algorithm, the bundle adjustment will be accelerated, whether the cameras are calibrated or un-calibrated. The proposed techniques for bundle adjustment are not only tolerant of missing data, but also allow the assignment of individual covariance to each image measure- ment. Experiments are conducted both on synthetic data and on real data to compare the proposed bundle adjustment techniques with the other existing methods. It is shown that the result obtained with the proposed techniques is comparable to that with the maximum likelihood estimation, however, it is more efficient.