Dynamic Incremental Data Summarization for Hierarchical Clustering
In many real world applications, with the databases frequent insertions and deletions, the ability of a data mining technique to detect and react quickly to dynamic changes in the data distribution and clustering over time is highly desired. Data summarizations (e.g., data bubbles) have been proposed to compress large databases into representative points suitable for subsequent hierarchical cluster analysis. In this paper, we thoroughly investigate the quality measure (data summarization index) of incremental data bubbles. When updating databases, we show which factors could affect the mean and standard deviation of data summarization index or not. Based on these statements, a fully dynamic scheme to maintain data bubbles incrementally is proposed. An extensive experimental evaluation confirms our statements and shows that the fully dynamic incremental data bubbles are effective in preserving the quality of the data summarization for hierarchical clustering.

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: 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: A High-Performance Intelligent Integrated Data Services System in Data Grid
abstract: It is very complex and of low efficiency for grid users to access heterogeneous diverse data resources distributed over the whole wide area network. The integrated data services can implement more wide range of transparent access to data resources in Data Grid. One of the important building blocks for any Data Grid is the server which implements data services. In this paper, we describe HPIIDS which implements integrated data services and achieves more wide range of transparencies. In order to improve its performances, we emphasis on the server called DSB which provides integrated data services. The DSB bases on cluster and agent technologies. Agent-based DSB can cleverly prefetch required data, replicate them among DSBs. Multiple DSBs are formed cluster HPIIDS. Cluster-based HPIIDS implements a single entry, provides a virtual integrated storage system for users, hides distributed heterogeneous low-level data resources, and insures load balance of each server. HPIIDS architecture supporting these various scenarios, are also described.

title: Entropy-based metrics in swarm clustering
abstract: Ant-based clustering methods have received significant attention as robust methods for clustering. Most ant-based algorithms use local density as a metric for determining the ants' propensities to pick up or deposit a data item; however, a number of authors in classical clustering methods have pointed out the advantages of entropy-based metrics for clustering. We introduced an entropy metric into an ant-based clustering algorithm and compared it with other closely related algorithms using local density. The results strongly support the value of entropy metrics, obtaining faster and more accurate results. Entropy governs the pickup and drop behaviors, while movement is guided by the density gradient. Entropy measures also require fewer training parameters than density-based clustering. The remaining parameters are subjected to robustness studies, and a detailed analysis is performed. 
 
In the second phase of the study, we further investigated Ramos and Abraham's ( Proc 2003   2003. pp 1370–1375) contention that ant-based methods are particularly suited to incremental clustering. Contrary to expectations, we did not find substantial differences between the efficiencies of incremental and nonincremental approaches to data clustering.

title: Database and location management schemes for mobile communications
abstract: Signaling traffic incurred in tracking mobile users and delivering enhanced services causes an additional load in the network. Efficient database and location management schemes are needed to meet the challenges from high density and mobility of users, and various service features. In this paper, the general location control and management function is treated as the combination of two parts, the global and local scope. New schemes and methods are proposed, and improvements achieved over established basic schemes are shown by using simulations.

title: Filtering Spam in Social Tagging System with Dynamic Behavior Analysis
abstract: Spam in social tagging systems introduced by some malicious participants has become a serious problem for its global popularizing. Some studies which can be deduced to static user data analysis have been presented to combat tag spam, but either they do not give an exact evaluation or the algorithms’ performances are not good enough. In this paper, we proposed a novel method based on analysis of dynamic user behavior data for the notion that users’ behaviors in social tagging system can reflect the quality of tags more accurately. Through modeling the different categories of participants’ behaviors, we extract tag-associated actions which can be used to estimate whether tag is spam, and then present our algorithm that can filter the tag spam in the results of social search. The experiment results show that our method indeed outperforms the existing methods based on static data and effectively defends against the tag spam in various spam attacks.

title: Development Of An Soi-Based Micro Check Valve
abstract: This paper presents a bulk micromachined check valve with very high frequency and extremely low leak rates. The valve is designed to have a hexagonal orifice, a hexagonal membrane flap and three flexible tethers. The three elbow-shaped flexible tethers are used both to secure the membrane flap to the valve seat and to abtain a large flap displacement in the forward flow direction. SOI wafer and DRIE technology are used to implement this micro valve. A very simple farbication process has been developed, and only two photolithographic masks are employed. Preliminary testing on a 1.5 milimeters size check valve shows that a maximum flow rate (DI water) of 35.6ml/min was obtained at pressure drop of 65.5kPa and negligible leakage rate in the reverse flow direction observed at pressure up to 600kPa.