Querying multiple sets of discovered rules
Rule mining is an important data mining task that has been applied to numerous real-world applications. Often a rule mining system generates a large number of rules and only a small subset of them is really useful in applications. Although there exist some systems allowing the user to query the discovered rules, they are less suitable for complex ad hoc querying of multiple data mining rulebases to retrieve interesting rules. In this paper, we propose a new powerful rule query language Rule-QL for querying multiple rulebases that is modeled after SQL and has rigorous theoretical foundations of a rule-based calculus. In particular, we first propose a rule-based calculus RC based on the first-order logic, and then present the language Rule-QL that is at least as expressive as the safe fragment of RC. We also propose a number of efficient query evaluation techniques for Rule-QL and test them experimentally on some representative queries to demonstrate the feasibility of Rule-QL.

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: 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: 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: A Graph-Based Algorithm for Mining Maximal Frequent Itemsets
abstract: Association rule mining is an important research branch of data mining, and computing frequent itemsets is the main problem. The paper is designed to find maximal frequent itemsets only. It presents an algorithm based on a frequent pattern graph, which can find maximal frequent itemsets quickly. A breadth-first-search and a depth-first-search techniques are used to produce all maximal frequent itemsets of a database. The paper also analyzes the complexity of the algorithm, and explains the computation procedure by examples. It has high time efficiency and less space complexity for computing maximal frequent itemsets.

title: A Vector Matrix Iterative Self-Organizing Assistant Clustering Algorithm of XML Document
abstract: To improve the clustering quality of massive extensible markup language (XML) document clustering, this paper proposes a vector matrix iterative self-organizing assistant clustering algorithm of XML document (VMISACAX). The algorithm bases on the XML key, and transforms XML document into vector matrix, then carries out the optimizations of canceling, dissociating and uniting etc. In order to improve the convergence of the algorithm, a assistant strategy is imported to shorten the algorithm time under settling for clustering, to obtain best result of clustering by XML key's weights, but it doesn't always obtain the maximum distance's target of matrix vector clustering. Contrasted with other vector clustering algorithms, a series of emulation experiments show that this algorithm has proper the effectiveness and feasibility.

title: Rate-Distortion Optimized Progressive Geometry Compression
abstract: During progressive transmission of 3D geometry models, the transmission order of details at different region has great effects on the quality of reconstructed models at low bit-rate. This work presents a ratedistortion (R-D) optimized progressive geometry compression scheme to improve the quality of reconstructed models by adjusting the transmission order of details. In this scheme, the input mesh is partitioned into parts, then each part is encoded into bit-stream independently, and the encoded bit-streams are truncated into segments while getting the R-D characteristics of every segment, at last all segments are assembled into a codestream based on R-D optimization, which ensure the region with rich detail will be transmitted early and make the reconstructed mesh achieve better quality as soon as possible. Experimental results show that, as compared with the well-known PGC method, the proposed one provides better R-D performance. Moreover, it provides a novel way to realize the region of interest (ROI) coding of 3D meshes. Rate-distortion optimization; Progressive compression; Mesh partition

title: Adaptive Resource Allocation in Multiuser OFDM System Based on Genetic Algorithm
abstract: Multiuser orthogonal frequency division multiplexing (MU-OFDM) is a promising technique for achieving high downlink capacities in future cellular and wireless local area network systems. The capacity of MU-OFDM can be maximized as long as subchannels and power are allocated adaptively, a new method based on genetic algorithm (GA) is proposed for adaptive power allocation in MU-OFDM system. Some proportional fairness constraints are also added to assure that each user can achieve a required data rate. Each subchannel to the user with the best channel-to-noise ratio has been assigned and an optimal power allocation algorithm based on GA has been proposed. The results demonstrate that the new method we proposed is optimal and efficient to resource allocation.

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.