Controlling FD and MVD Inferences in MLS

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: 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: Design and Implementation of Control System on Embedded Downloading Server Based on C/S Architecture
abstract: Based on Client-Server architecture, this paper proposes the design and implementation of system controlling embedded-Linux downloading server. The software architecture design of this system is composed of three parts: network layer, control layer, and application layer, which is modular and pluggable. The implementation also proved the feasibility, reliability and portability of this design.

title: An Efficient and Accurate Method for 3D-Point Reconstruction from Multiple Views
abstract: In this paper we consider the problem of finding the position of a point in space given its projections in multiple images taken by cameras with known calibration and pose. Ideally the 3D point can be obtained as the intersection of multiple known rays in space. However, with noise the rays do not meet at a single point generally. Therefore, it is necessary to find a best point of intersection. In this paper we propose a modification of the method (Ma et al., 2001. Journal of Communications in Information and Systems, (1):51–73) based on the multiple-view epipolar constraints. The solution is simple in concept and straightforward to implement. It includes generally two steps: first, image points are corrected through approximating the error model to the first order, and then the 3D point can be reconstructed from the corrected image points using any generic triangulation method. Experiments are conducted both on simulated data and on real data to test the proposed method against previous methods. It is shown that results obtained with the proposed method are consistently more accurate than those of other linear methods. When the measurement error of image points is relatively small, its results are comparable to those of maximum likelihood estimation using Newton-type optimizers; and when processing image-point correspondences cross a small number of views, the proposed method is by far more efficient than the Newton-type optimizers.

title: Particle swarm optimization for function optimization in noisy environment
abstract: As a novel evolutionary searching technique, particle swarm optimization (PSO) has gained wide research and effective applications in the field of function optimization. However, to the best of our knowledge, most studies based on PSO are aimed at deterministic optimization problems. In this paper, the performance of PSO for function optimization in noisy environment is investigated, and an effective hybrid PSO approach named PSOOHT is proposed. In the PSOOHT, the population-based search mechanism of PSO is applied for well exploration and exploitation, and the optimal computing budget allocation (OCBA) technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good particles. Meanwhile, hypothesis test (HT) is also applied in the hybrid approach to reserve good particles and to maintain the diversity of the swarm as well. Numerical simulations based on several well-known function benchmarks with noise are carried out, and the effect of noise magnitude is also investigated as well. The results and comparisons demonstrate the superiority of PSOOHT in terms of searching quality and robustness.

title: Classification rule discovery with ant colony optimization
abstract: Ant-based algorithms or ant colony optimization (ACO) algorithms have been applied successfully to combinatorial optimization problems. More recently, Parpinelli and colleagues applied ACO to data mining classification problems, where they introduced a classification algorithm called Ant/spl I.bar/Miner. In this paper, we present an improvement to Ant/spl I.bar/Miner (we call it Ant/spl I.bar/Miner3). The proposed version was tested on two standard problems and performed better than the original Ant/spl I.bar/Miner algorithm.

title: Positive solutions of a nonlinear four-point boundary value problems
abstract: In this paper, by using the Krasnoselskii's theorem in a cone, we study the existence of at least one or two positive solutions to the four-point boundary value problemy^''(t)+a(t)f(y(t))=0,00. As an application, we also give some examples to demonstrate our results.