Mining community structure of named entities from free text
Although community discovery has been studied extensively in the Web environment, limited research has been done in the case of free text. Co-occurrence of words and entities in sentences and documents usually implies connections among them. In this paper, we investigate the co-occurrences of named entities in text, and mine communities among these entities. We show that identifying communities from free text can be transformed into a graph clustering problem. A hierarchical clustering algorithm is then proposed. Our experiment shows that the algorithm is effective to discover named entity communities from text documents.

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: 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: Measuring the meaning in time series clustering of text search queries
abstract: We use a combination of proven methods from time series analysis and machine learning to explore the relationship between temporal and semantic similarity in web query logs; we discover that the combination of correlation and cycles is a good, but not perfect, sign of semantic relationship.

title: An Energy-Minimizing Mesh Parameterization
abstract: In this paper, we propose a new energy-minimizing mesh parameterization method, which linearly combines two new energies EQ and EM. It not only avoids triangles overlap in the parameter domain, but also is invariant under rotation, translation and scale transformations. We first parameterize the original 3D mesh to the parameter plane by using the energy-minimizing parameterization, and get the optimal effect by optimizing the weights wij gradually. Experimental results indicate that this optimized energy-minimizing method has low distortion and good stability.

title: A Population-Based Incremental Learning Algorithm with Elitist Strategy
abstract: The population-based incremental learning (PBIL) is a novel evolutionary algorithm combined the mechanisms of the Genetic Algorithm with competitive learning. In this paper, the influence of the number of selected best solutions on the convergence speed of the PBIL is studied by experiment. Based on experimental results, a PBIL algorithm with elitist strategy, named Double Learning PBIL (DLPBIL), is proposed. The new algorithm learns both the selected best solutions in current population and the optimal solution found so far in the algorithm at same time. Experimental results show that the DLPBIL out-performs the standard PBIL. Both the convergence speed and the solution quality are improved.

title: Solvability of multi-point boundary value problem at resonance (III)
abstract: In this paper, we consider the following second order ordinary differential equation(1.1)x^'^'=f(t,x(t),x^'(t))+e(t),t@?(0,1),subject to one of the following boundary value conditions: (1.2)x(0)=@?"i"="1^m^-^2@a"ix(@x"i),x(1)=@?"j"="1^n^-^2@b"jx(@h"j),(1.3)x(0)=@?"i"="1^m^-^2@a"ix(@x"i),x^'(1)=@?"j"="1^n^-^2@b"jx^'(@h"j),(1.4)x^'(0)=@?"i"="1^m^-^2@a"ix^'(@x"i),x(1)=@?"j"="1^n^-^2@b"jx(@h"j),where @a"i(1=

title: WBEM Based Distributed Network Monitoring
abstract: In the paper we identify the needs in efficient management of distributed networks and some problematic areas in the field. Then we introduce Web Based Enterprise Management (WBEM) to address the problem of providing a unified way to model all kinds of managed elements in a single information model in heterogeneous network environments. The advantages brought about by the use of WBEM in network management solve some critical problems existing in current network management. Based on the brief description of components of WBEM, we discuss in depth the basic WBEM instrumentation and multi-tiered WBEM enabled management infrastructure. We also apply this multi-tiered management infrastructure to a network management application scenario to monitor network activities for unexpected behaviors.