WebKDD 2004: web mining and web usage analysis post-workshop report
In this report, we summarize the contents and outcomes of the recent WebKDD 2004 workshop that was held in conjunction with the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), August 22-25, 2004, in Seattle, Washington. We also reflect on the trend in participation levels in the WebKDD series of workshops over the last six years, and indicate new directions in Web mining research as reflected in the latest workshop.

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: 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: 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: Prediction and Analysis of Landslide Based on Fuzzy Theory
abstract: There are internal and external reasons for the occurrence of landslip. Through the analysis on a large number of investigation materials of landslip, the growth inducements of landslip are found out. By use of theories in blur maths, we conduct order arrangement of these inducements in accordance their significance, put forward blur judgment rule of landslip estimation and present applicable examples.

title: Semi-online Task Allocation Algorithm among Cooperative Agents
abstract: Task allocation algorithm has great influence on the efficiency multi-agent task system. The performance of the existing allocation algorithms will decline with the increasing of task complexity. So a task allocation framework is presented and a semi-online multi-agent task allocation algorithm(SOAL) based on dependences of sub-tasks is proposed, the relationship of dependences among sub-tasks is the partial knowledge for SOAL. In contrast to the existing approaches, the performance of SOAL is more close to the optimal offline algorithm. The competitive analysis results and the tests of time performance demonstrate the advantage of SOAL

title: DE and NLP Based QPLS Algorithm
abstract: As a novel evolutionary computing technique, Differential Evolution (DE) has been considered to be an effective optimization method for complex optimization problems, and achieved many successful applications in engineering. In this paper, a new algorithm of Quadratic Partial Least Squares (QPLS) based on Nonlinear Programming (NLP) is presented. And DE is used to solve the NLP so as to calculate the optimal input weights and the parameters of inner relationship. The simulation results based on the soft measurement of diesel oil solidifying point on a real crude distillation unit demonstrate that the superiority of the proposed algorithm to linear PLS and QPLS which is based on Sequential Quadratic Programming (SQP) in terms of fitting accuracy and computational costs.

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: 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.