Analyzing the Subjective Interestingness of Association Rules
Association rules, a class of important regularities in databases, have proven very useful in practical applications, but association-rule-mining algorithms tend to produce huge numbers of rules, most of which are of no interest. Users have considerable difficulty manually analyzing so many rules to identify the truly interesting ones. To solve that problem, we have developed a new approach to help them find interesting rules (in particular, unexpected rules) from a set of discovered association rules. This interestingness analysis system (IAS) leverages the user's existing domain knowledge to analyze discovered associations and then rank discovered rules according to various interestingness criteria, such as conformity and various types of unexpectedness. This article describes how we have implemented this technique and used it successfully in a number of applications.

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: 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: 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: 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: ALHACF: An Anonymity-Level Selected Hierarchical Anonymous Communication Framework
abstract: With the more emphasis paid on network security and privacy, the research on anonymous communication system has developed quickly. In the past, researchers mainly focused on how to improve the anonymity, but few focused on the usability. This paper presents an anonymity-level selected hierarchical anonymous communication framework (ALHACF). In this framework, firstly the anonymity-level is selected by users according to his requirement, and then the path length and set of relay routers will be determined by the anonymity-level. The anonymity and communication performance of ALHACF are analyzed. The analysis indicates that ALHACF inherits the merit of HLLACF, and it can prevent some kinds of attack, keep good anonymity and increase the communication performance markedly.

title: Semantic-Based Remote Sensing Images Intelligent Service on Grid Environment
abstract: In this work, we studied how to rapidly match remote sensing images by the semantic information of geographical objects in Grid architecture and how to slice, index, and assemble each tiled image in every grid node. We first designed the grid architecture of remote sensing images sharing and gave a new idea that searching corresponding images by semantic information of geographical objects. To each grid node, we put forward a new method to partition, index, organize and assemble remote sensing images by rectangle cell. Then, a prototype system was set up: five grid node machines and a grid Server were employed to establish the remote sensing images service grid environment. Massive remote sensing images were treated with new methods above in grid node and vector data managed in grid server to supply semantic information. The tested results proved that goal remote sensing images were searched with higher speed and more accuracy in grid environment and each grid node had faster response compared with traditional remote sensing images sharing mode. And more intelligent services of remote sensing images were given for the multi-resolution, multi-temporal tiled images can be dynamically assembled and analyzed. Experiments proved it was a promising solution to provide remote sensing images services using Grid architecture in light of actual circumstances.

title: Support Vector Machines for Text Categorization in Chinese Question Classification
abstract: Question classification plays a crucial important role in the question answering system because categorizing a given question is beneficial to identify an answer in the documents. The goal of question classification is to accurately assign labels to question based on expected answer type. Recently, many machine learning algorithms are used for question classification. However many research results show that SVM perform best in this task, because it is well known to work well for nonlinear, sparse, high dimensional problems. In this experiment, we perform the One-against-One SVM algorithm and a feature extraction method of Chinese questions to get high classification accuracy

title: Twi-Map Support Vector Machine for Multi-classification Problems
abstract: In this paper, a novel method called Twi-Map Support Vector Machines (TMSVM) for multi-classification problems is presented. Our ideas are as follows: Firstly, the training data set is mapped into a high-dimensional feature space. Secondly, we calculate the distances between the training data points and hyperplanes. Thirdly, we view the new vector consisting of the distances as new training data point. Finally, we map the new training data points into another high-dimensional feature space with the same kernel function and construct the optimal hyperplanes. In order to examine the training accuracy and the generalization performance of the proposed algorithm, One-against-One algorithm, Fuzzy Least Square Support Vector Machine (FLS-SVM) and the proposed algorithm are applied to five UCI data sets. Comparison results obtained by using three algorithms are given. The results show that the training accuracy and the testing one of the proposed algorithm are higher than those of One-against-One and FLS-SVM.

title: ECO: an empirical-based compilation and optimization system
abstract: In this paper, we describe a compilation system that automates much of the process of performance tuning that is currently done manually by application programmers interested in high performance. Due to the growing complexity of accurate performance prediction, our system incorporates empirical techniques to execute variants of code segments with representative data on the target architecture. In this paper, we discuss how empirical techniques and performance modeling can be effectively combined. We also discuss the role of historical information from prior runs, and programmer specifications supporting run-time adaptation. These techniques can be employed to alleviate some of the performance problems that lead to inefficiencies in key applications today: register pressure, cache conflict misses, and the trade-off between synchronization, parallelism and locality in SMPs.