Top 10 algorithms in data mining
This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

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: 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: 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: Performance Analysis of the HLLACF
abstract: With the popular using of anonymous communication systems, security and overhead traffic attract more attention. HLLCAF was presented to improve the performance of anonymous communication systems. This paper analyzes HLLACF's security and an evaluation algorithm for security is presented. In the end, a simulation experiment and result analysis is given. The theoretic analysis and simulation experiment indicate that the HLLACF can prevent AS-Level passive attack and other similar attacks well while decreasing communication delay and HLLACF also scales well.

title: Multi-agent System for Custom Relationship Management with SVMs Tool
abstract: Distributed data mining in the CRM is to learn available knowledge from the customer relationship so as to instruct the strategic behavior. In order to resolve the CRM in distributed data mining, this paper proposes the architecture of distributed data mining for CRM, and then utilizes the support vector machine tool to separate the customs into several classes and manage them. In the end, the practical experiments about one Chinese company are conducted to show the good performance of the proposed approach.

title: Manufacturing Grid: Needs, Concept, and Architecture
abstract: As a new approach, grid technology is rapidly used in scientific computing, large-scale data management, and collaborative work. But in the field of manufacturing, the application of grid is just at the beginning. The paper proposes the concept of manufacturing. The needs, definition and architecture of manufacturing gird are discussed, which explains why needs manufacturing grid, what is manufacturing grid and how to construct a manufacturing grid system.

title: Hybrid differential evolution for noisy optimization
abstract: A robust hybrid algorithm named DEOSA for function optimization problems is investigated in this paper. In recent years, differential evolution (DE) has attracted wide research and effective applications in various fields. However, to the best of our knowledge, most of the available works did not consider noisy and uncertain environments in practical optimization problems. This paper focuses on a robust DE, which can adapt to noisy environment in real applications. By combining the advantages of DE algorithm, the optimal computing budget allocation (OCBA) technique and simulated annealing (SA) algorithm, a robust hybrid DE approach DEOSA is proposed. In DEOSA, the population-based search mechanism of DE is applied for well exploration and exploitation, and the OCBA technique is used to allocate limited sampling budgets to provide reliable evaluation and identification for good individuals. Meanwhile, SA is also applied in the hybrid approach to maintain the diversity of the population, in order to alleviate the negative influences on greedy selection mechanism of DE brought by the noises. DEOSA is tested by well-known benchmark problems with noise and the effect of noise magnitude is also investigated. The comparisons to several commonly used techniques for optimization in noisy environment are also carried out. The results and comparisons demonstrate the superiority of DEOSA.

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.