Discovering Frequent Substructures from Hierarchical Semi-structured Data
Frequent substructure discovery from a collection of semi-structured objects can serve for storage, browsing, querying, indexing and classification of semi-structured documents. This paper examines the problem of discovering frequent substructures from a collection of hierarchical semi-structured objects of the same type. The use of wildcard is an important aspect of substructure discovery from semi-structured data due to the irregularity and lack of fixed structure of such data. This paper proposes a more general and powerful wildcard mechanism, which allows us to find more complex and interesting substructures than existing techniques. Furthermore, the complexity of structural information of semi-structured data and the usage of wildcard make the existing frequent set mining algorithms inapplicable for substructure discovery. In this work, we adopt a vertical format for the storage of semi-structured objects, and adapt a frequent set mining algorithm for our purpose. The application of our approach to real-life data shows that it is very effective.

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: 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: 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: Non-linear Correlation Techniques in Educational Data Mining
abstract: There is such an increasing interest in data mining and educational systems currently that has made educational data mining as a new growing research community. This paper explores how to develope new methods for discovering knowledge of data from educational context. The non-linear correlation technology was introduced and applied in the mining process in the whole knowledge achieved. Meanwhile, we have applied these methods in the real course management datasets and found correspondent results for the educators.

title: Numerical Experiments with Monte Carlo Methods and SPAI Preconditioner for Solving System of Linear Equations
abstract: In this paper we present the results of experiments comparing the performance of the mixed Monte Carlo algorithms and SPAI preconditener with BICGSTAB. The experiments are carried out on a Silicon Graphics ONYX2 machine. Based on our experiments, we conclude that these techniques are comparable from the point of view of robustness and rates of convergence, with the Monte Carlo approach performing better for some general cases and SPAI approach performing better in case of very sparse matrices.

title: Content-based indexing and retrieval method of Chinese document images
abstract: In Chinese information retrieval, it is easy to index a Chinese text document for retrieval. We just need to segment the text document into phrases. When the document is a Chinese document image (non-ASCII file), we may first convert the document image into the text file by using Chinese optical character recognition (OCR) technology and then index the document by using an information retrieval algorithm. However, OCR needs more time, which can influence retrieval efficiency. This paper proposes an index method based on stroke density code. First segment the document image to get all the Chinese character images, then calculate the stroke density of each Chinese character image, and at last attain the stroke density code of the character image. The index method has the advantage of speed and robustness to noise. In addition, this paper also offers a retrieval method for Chinese document images based on the index technology. We discuss the index and retrieval method for duplicate detection. We have proved the validity of the index method through its application to keyword spotting and duplicate detection.

title: A Study on Information Extraction from PDF Files
abstract: Portable Document Format (PDF) is increasingly being recognized as a common format of electronic documents. The prerequisite to management and indexing of PDF files is to extract information from them. This paper describes an approach for extracting information from PDF files. The key idea is to transform the text information parsed from PDF files into semi-structured information by injecting additional uniform tags. An extensible rule set is built on tags and other knowledge. Guided by the rules, one pattern matching algorithm based on a tree model is applied to obtain the necessary information. A further experiment proved that this method was effective.

title: A service-oriented business performance evaluation model and the performance-aware service selection method
abstract: In service-oriented architecture, services and business processes are closely related and therefore the research on service-oriented business process (SOBP) attracts the attention of academia as well as industry. Because of the loosely coupled, autonomic and dynamic nature of services, the operation and the performance evaluation of business process meet some challenges, such as the definition of the key performance indicators (KPIs) and the alignment of business performance and IT performance. In this paper, we address these challenges. First, the definition and the characteristics of an SOBP are presented with a motivating scenario. Then a service-oriented business performance evaluation model is described, which integrates the performance of strategy layer, business process layer, business activity layer, service composition layer and IT infrastructure layer. The KPIs corresponding to each layer are also defined with their formal representations. The qualitative and quantitative performance analysis methods, based on the proposed model, are presented, respectively. Finally, the improved analytic hierarchy process is explicated to calculate the correlation between different KPIs and select the most suitable service. An online-shopping example is taken to prove the soundness and the feasibility of the method.