Wei Guang Teng

A regression-based temporal pattern mining scheme for data streams

We devise in this paper a regression-based algorithm, called algorithm FTP-DS (Frequent Temporal Patterns of Data Streams), to mine frequent temporal patterns for data streams. While providing a general framework of pattern frequency counting, algorithm FTP-DS has two major features, namely one data scan for online statistics collection and regression-based compact pattern representation.To attain the feature of one data scan, the data segmentation and the pattern growth scenarios are explored for the frequency counting purpose. Algorithm FTP-DS scans online transaction flows and generates candidate frequent patterns in real time. The second important feature of algorithm FTP-DS is on the regression-based compact pattern representation. Specifically, to meet the space constraint, we devise for pattern representation a compact ATF (standing for Accumulated Time and Frequency) form to aggregately comprise all the information required for regression analysis. In addition, we develop the techniques of the segmentation tuning and segment relaxation to enhance the functions of FTP-DS. With these features, algorithm FTP-DS is able to not only conduct mining with variable time intervals but also perform trend detection effectively. Synthetic data and a real dataset which contains net-Permission work alarm logs from a major telecommunication company are utilized to verify the feasibility of algorithm FTP-DS.

Integrating Web caching and Web prefetching in client-side proxies

Web caching and Web prefetching are two important techniques used to reduce the noticeable response time perceived by users. Note that by integrating Web caching and Web prefetching, these two techniques can complement each other since the Web caching technique exploits the temporal locality, whereas Web prefetching technique utilizes the spatial locality of Web objects. However, without circumspect design, the integration of these two techniques might cause significant performance degradation to each other. In view of this, we propose in this paper an innovative cache replacement algorithm, which not only considers the caching effect in the Web environment, but also evaluates the prefetching rules provided by various prefetching schemes. Specifically, we formulate a normalized profit function to evaluate the profit from caching an object (i.e., either a nonimplied object or an implied object according to some prefetching rule). Based on the normalized profit function devised, we devise an innovative Web cache replacement algorithm, referred to as Algorithm IWCP (standing for the Integration of Web Caching and Prefetching). Using an event-driven simulation, we evaluate the performance of Algorithm IWCP under several circumstances. The experimental results show that Algorithm IWCP consistently outperforms the companion schemes in various performance metrics.

On the mining of substitution rules for statistically dependent items

In this paper a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of items with that of others. The process of mining substitution rules can be decomposed into two procedures. The first identifies concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second is substitution rule generation. Two concrete itemsets X and Y form a substitution rule, denoted by X /spl utri/ Y to mean that X is a substitute for Y if and only if X and Y are negatively correlated and the negative association rule X /spl rarr/ Y~ exists. We derive theoretical properties for the model of substitution rule mining. Then, in light of these properties, the SRM algorithm (substitution rule mining) is designed and implemented to discover substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of the SRM algorithm. It is shown that SRM produces substitution rules of very high quality.

Exploiting the Self-Organizing Map for Medical Image Segmentation

As the computer technology advances, data acquisition, processing and visualization techniques have a tremendous impact on medical imaging. On the other hand, however, the interpretation of medical images is still almost performed by radiologists nowadays. Developments in artificial intelligence and image processing show that computer-aided diagnosis emerges with increasingly high potential. In this paper, we develop an intelligent approach to perform image segmentation and thus to discover region of interest (ROI) for diagnosis purposes through the use of self-organizing map (SOM) techniques. Specifically, we propose a two-stage SOM approach which can precisely identify dominant color components and thus segment a medical image into several smaller pieces. In addition, with a proper merging step conducted iteratively, one or more ROIs in a medical image can usually be identified. Empirical studies show that our approach is effective at processing various types of medical images. Moreover, the feasibility of our approach is also evaluated by the illustration of image semantics.

Incorporating Multi-Criteria Ratings in Recommendation Systems

Recommendation systems utilize information techniques to the problem of helping users find the items they would like. Example applications include the recommendation systems for movies, books, CDs and many others. As recommendation systems emerge as an independent research area, the rating structure plays a critical role in recent studies. Among many alternatives, the collaborative filtering algorithms are generally accepted to be successful to estimate user ratings of unseen items and then to derive proper recommendations. In this paper, we extend the concept of single criterion ratings to multi-criteria ones, i.e., an item can be evaluated in many different, aspects. Since there are usually conflicts among different criteria, the recommendation problem cannot be formulated as an optimization problem any more. Instead, we propose to use data query techniques to solve this multi-criteria recommendation problem.

A statistical framework for mining substitution rules

In this paper, a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of some items with that of others. The mining of substitution rules in a transaction database, the same as that of association rules, will lead to very valuable knowledge in various aspects, including market prediction, user behaviour analysis and decision support. The process of mining substitution rules can be decomposed into two procedures. The first procedure is to identify concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second procedure is then on the substitution rule generation. In this paper, we first derive theoretical properties for the model of substitution rule mining and devise a technique on the induction of positive itemset supports to improve the efficiency of support counting for negative itemsets. Then, in light of these properties, the SRM (substitution rule mining) algorithm is designed and implemented to discover the substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of the SRM algorithm proposed. It is shown that the SRM algorithm not only has very good execution efficiency but also produces substitution rules of very high quality.

Mining communities of acquainted mobile users on call detail records

In a telecommunication system, call detail records (i.e., CDRs) are generated automatically for tracking and billing purposes when mobile users having calls. To further investigate the information buried in huge amounts of CDRs, relationship among mobile users can be organized. Specifically, communities of acquainted mobile users can be effectively discovered from collected CDRs through our approach proposed in this paper. Note that understanding the communities and corresponding calling behaviors are of great importance to telecommunication companies. To conduct proper community mining on CDRs, techniques of data transformation and social network analysis are fully exploited. Our study shows that the proposed approach is practically feasible.

Pricing and inventory policies for Hi-tech products under replacement warranty

Companies, especially in the Hi-tech (high-technology) industry (such as computer, communication and consumer electronic products), often provide a replacement warranty period for purchased items. In reality, simultaneously determining the price and inventory decisions under warranty policy is an important issue. The objective of this paper is to develop a joint pricing and inventory model for Hi-tech products under replacement warranty policy. In the first model, we consider a Hi-tech product feature in which the selling price is declining in a trend. We determine the optimal inventory level for each period and retail price for the first period while maximising the total profit. In the second model, we further determine the optimal retail price and inventory level for each period in the dynamic demand market. This study develops solution approaches to solve the problems described above. Numerical analysis discusses the influence of system parameters on the companys decisions and behaviours. The results of this study could serve as a reference for business managers or administrators.

Identifying Regions of Interest in Medical Images Using Self-Organizing Maps

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

Extending Web Search for Online Plagiarism Detection

As information technologies advance, the data amount gathered on the Internet increases at an incredible rapid speed. To solve the data overloading problem, people commonly use Web search engines to find what they need. However, as search engines become an efficient and effective tool, plagiarists can grab, reassemble and redistribute text contents without much difficulty. In this paper, we develop an online detection system to reduce such misapplication of search engines. Specifically, suspicious documents are extracted and verified through the collaboration of our plagiarism detection system and search engines. With a proper design, extracted text segments are given different priorities when sending them to search engines as the ascertainment of plagiarism. This greatly reduces unnecessary and repetitive works when performing plagiarism detection.