Intelligent air travel and tourist information systems

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: 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: 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: 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: Collective Behavior of Dynamic Swarm with General Topology and Complex Communication Time-Delays
abstract: This paper presents a complex interaction delayed swarm model with a general topology to study the collective behavior of a group of autonomous agents using the nearest neighbor rulers in the presence of communication delays. It is proved that under certain conditions the swarm members will converge to a finite region around the swarm weighted center and move together in a cohesive cluster following the motion of the weighted center. For general cases, the delayed swarm may display more complex dynamics, including oscillation and divergence, depending on the delay values. This suggests that the time delay may have significant consequence in the collective dynamics of swarms.

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: Flocking of Multi-Vehicle Systems With A Leader
abstract: We study the coordinated motion of a group of vehicles with a leader based on nearest neighbor rules. The leader may be a special vehicle in the group or an external signal (virtual leader) to steer the vehicle group. The control law consists of two parts: a potential field force that makes the vehicles attract to each other and at the same time avoid collision in the group, and an alignment force that makes all vehicles' headings and speeds to converge respectively to common values. We assume that the interaction patterns are either fixed or switching based on the information topologies of the leader and member vehicles. Our approach is based on the Lyapunov theory and basic graph theory. We also consider the effect of noise on the collective dynamics of the group. Numerical simulations are worked out to illustrate the analytical results

title: A New Parallel Segmentation Model Based on Dictionary and Mutual Information
abstract: It is difficult to compute the word frequency for mutual information segmentation. Statistic of word frequency of parallel mutual information is integrated with dictionary segmentation to improve efficiency in this paper. The parallel model and dispatching policy are presented, the paper also gives the speed up ratio of parallel model at the same time, periods pattern string and non periods pattern string are optimized in parallel model. Experiment show that the algorithm is available. The parallel model also can use for other segmentation algorithms that base on statistic of word frequency.

title: Distributed Intelligent Network Management Model for the Large-Scale Computer Network
abstract: Computer networks have become large-scale distributed systems and network management will develop towards the distributed management and the intelligent management. Multi-agent based Distributed Intelligent Network Management model (MDINM) for the large-scale computer network, which utilizes key characteristics of the agent, such as autonomy, reactivity, and mobility, is put forward in this paper. This model can not only avoid transmitting a large amount of original data on the network to realize the distributed network management but also provides network management with more intelligence by realizing the dynamic self-management for the network. The experimental results show that MDINM is better than the existing network management solutions on performance for the large-scale computer network.