Association Link Network Based Concept Learning in Patent Corpus

Concept learning has attracted considerable attention as a means to tackle problems of representation and learning corpus knowledge. In this paper, we investigate a challenging problem to automatically construct a patent concept learning model. Our model consists of two main processes; which is the acquisition of the initial concept graph and refined process for the initial concept graph. The learning algorithm of a patent concept graph is designed based on the Association Link Network (ALN). A concept is usually described by multiple documents utilizing ALN here in concept learning. We propose a mixture-ALN, which add links between documents and the lexical level, compared with the ALN. Then, a heuristic algorithm is proposed to refine the concept graph, leading to a more concise and simpler knowledge for the concept. The heuristic algorithm consists of four phases; first, for simplifying bag of words for concept in patent corpus, we start to select a core node from the initial concept graph. Second,for learning the association rule for the concept, we searched important association rules around the core node in our rules collection. Third, to ensure coherent semantics of the concept, we selected corresponding documents based on the selected association rules and words. Finally, for enriching semantics of the refined concept, we iteratively selected core nodes based on the corresponding documents and restarted our heuristic algorithm. In the experiments, our model shows effectiveness and improvements in prediction accuracy in the retrieve task of the patent.     

Building Web Knowledge Flows based on Interactive Computing with Semantics

Web personalized services alleviate the burden of information overload by providing right information which meets individual user’s needs. How to obtain and represent knowledge needed by users is a key issue. This paper presents Web Knowledge Flow (WKF) to represent the specific knowledge on Web pages and a model of Interactive Computing with Semantics (ICS) to provide a feasible means of generating WKF. Objective WKF (OWKF) and Real-time WKF (RWKF) are firstly proposed to satisfy staged and real-time user interests. Secondly, the generation algorithm of WKF is proposed based on Semantics Link Network. Thirdly, “interactive point” is introduced to detect the moment of user interests change to ensures the dynamics of WKF. Experimental results demonstrate that ICS can effectively capture the change of user interests and the generated WKF can satisfy user requirements accurately.     

基于Web的网络自主学习平台设计

随着网络技术的飞速发展,网络学习已成为当今教育的发展趋势,针对网络教育的资源共享和自由交流的优势,在分析传统教学存在学习资源不能充分共享、学习评价反馈不够及时等问题的基础上,提出了基于Web的网络自主学习平台,实现资源利用最大化、学习行为自主化、学习形式交互化等目的。     

基于关联规则的贝叶斯网络分类器

关联规则分类器(CBA)利用关联规则来构造分类算法,但其没有考虑分类问题中的不确定性.提出一种基于关联规则的贝叶斯网络分类算法.该算法利用关联规则挖掘算法提取初始的候选网络边集,通过贪心算法学习网络结构,得到比经典的贝叶斯网络分类器TAN更好的拓扑结构.通过在15个UCI数据集上的实验结果表明,该算法取得了比TAN,CBA更好的分类性能.     

Analysis and modeling of the semantically associated network on the Web

The semantically associated network on the Web is a Semantic Link Network built by mining the associated relation between Web pages. The associated link from page A to page B indicates that users who have browsed page A is likely to also browse page B. This paper explores the statistical properties of the associated network on the Web. Web pages of a specific domain are automatically downloaded by a Web crawler to build an associated network. We analyze the associated network at different domain thresholds and classify the topology into three states, that is, the original state, the kernel state and the final state. A mathematical model is built to study the in‐degree distribution, the out‐degree distribution and the total‐degree distribution for both the kernel state and the final state. By tuning the model parameters to reasonable values, we obtain the distinct power‐law forms for the three degree distributions with exponents that agree well with the statistical data. The proposed model can not only describe the evolving processes of the associated network on the Web, but also provides theory basis for complex applications such as semantic community discovery, intelligent browsing and recommendation. Copyright © 2009 John Wiley & Sons, Ltd.     

电子商务网络资讯管理系统的设计与实现

为了采集和管理电子商务类课程教学相关的网络资源,同时为文本聚类研究收集实验数据,采用Struts框架、Java语言和mysql后台数据库,设计和开发了电子商务网络资讯管理平台,实现了网页内容提取、网页关键词提取、资源检索、资源下载和评论等功能。系统满足了辅助课程教学和提供课题研究实验数据的实际需要,功能简洁实用,界面友好,运行稳定。     

Extraction and optimization of fuzzy association rules using multi-objective genetic algorithm

Association Rule Mining is one of the important data mining activities and has received substantial attention in the literature. Association rule mining is a computationally and I/O intensive task. In this paper, we propose a solution approach for mining optimized fuzzy association rules of different orders. We also propose an approach to define membership functions for all the continuous attributes in a database by using clustering techniques. Although single objective genetic algorithms are used extensively, they degenerate the solution. In our approach, extraction and optimization of fuzzy association rules are done together using multi-objective genetic algorithm by considering the objectives such as fuzzy support, fuzzy confidence and rule length. The effectiveness of the proposed approach is tested using computer activity dataset to analyze the performance of a multi processor system and network audit data to detect anomaly based intrusions. Experiments show that the proposed method is efficient in many scenarios.

SiteHelper: a localized agent that helps incremental exploration of the World Wide Web

The World Wide Web (the Web for short) is rapidly becoming an information flood as it continues to grow exponentially. This causes difficulty for users to find relevant pieces of information on the Web. Search engines and robots (spiders) are two popular techniques developed to address this problem. Search engines are indexing facilities over searchable databases. As the Web continues to expand, search engines are becoming redundant because of the large number of Web pages they return for a single search. Robots are similar to search engines; rather than indexing the Web, they traverse (“walk through”) the Web, analyzing and storing relevant documents. The main drawback of these robots is their high demand on network resources that results in networks being overloaded. This paper proposes an alternate way in assisting users in finding information on the Web. Since the Web is made up of many Web servers, instead of searching all the Web servers, we propose that each server does its own housekeeping. A software agent named SiteHelper is designed to act as a housekeeper for the Web server and as a helper for a Web user to find relevant information at a particular site. In order to assist the Web user finding relevant information at the local site, SiteHelper interactively and incrementally learns about the Web user's areas of interest and aids them accordingly. To provide such intelligent capabilities, SiteHelper deploys enhanced HCV with incremental learning facilities as its learning and inference engines.     

Analysis and design of mobile Wireless Social Model

Delay Tolerant Networks (DTNs) are characterized by very long delay paths and frequent network partitions. Most existing protocols are based on simplistic models which differ from real scenarios or real traces which do not allow for sensitivity analysis. In this paper, we analyze the differences between the Social Network (SN) and the Wireless Social Network (W-SN) in terms of the clustering coefficient, characteristic path length, community size, and membership number. We observe that the clustering coefficient, community size and membership number in the W-SN are bigger than those in the SN, while the character path length in W-SN becomes smaller. And then we propose a new Wireless Social Model (WSM) based on community character. Node in WSM is driven by the α-Model and has an equal probability to move to one of his adjacent vertices. We evaluate our model via real traces supplied by Dartmouth College, which shows that our wireless social mobile model offers a good approximation of real movements.     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

A Pre-Processed Cross Link Detection Protocol for geographic routing in mobile ad hoc and sensor networks under realistic environments with obstacles

In mobile ad hoc and sensor networks, greedy-face-greedy (GFG) geographic routing protocols have been a topic of active research in recent years. Most of the GFG geographic routing protocols make an ideal assumption that nodes in the network construct a unit-disk graph, UDG, and extract a planar subgraph from the UDG for face routing. However, the assumption of UDG may be violated in realistic environments, which may cause the current GFG geographic routing protocols to fail. In this paper, we propose a Pre-Processed Cross Link Detection Protocol, PPCLDP, which extracts an almost planar subgraph from a realistic network graph, instead of a UDG, for face routing and makes the GFG geographic routing work correctly in realistic environments with obstacles. The proposed PPCLDP improves the previous work of Cross Link Detection Protocol, CLDP, with far less communication cost and better convergence time. Our simulation results show that the average communication cost and the average convergence time of PPCLDP are, respectively, 65% and 45% less than those of CLDP. This makes PPCLDP more desirable for mobile ad hoc and sensor networks.     

一种协同学习信息RSS聚合器的设计

基于Web Service的网络协同学习信息RSS聚合平台可以为网络学习支持环境中的协同学习提供信息定制服务.本文研究运用RSS技术作为网络学习支持环境中协同学习信息传递方式,使用Web Service技术为网络学习环境提供分布式信息处理,提供扩展接口和跨平台性.     

Energy based competitive learning

This paper addresses the three important issues associated with competitive learning clustering, which are auto-initialization, adaptation to clusters of different size and sparsity, and eliminating the disturbance caused by outliers. Although many competitive learning methods have been developed to deal with some of these problems, few of them can solve all the three problems simultaneously. In this paper, we propose a new competitive learning clustering method termed energy based competitive learning (EBCL) to simultaneously tackle these problems. Auto-initialization is achieved by extracting samples of high energy to form a core point set, whereby connected components are obtained as initial clusters. To adapt to clusters of different size and sparsity, a novel competition mechanism, namely, size-sparsity balance of clusters (SSB), is developed to select a winning prototype. For eliminating the disturbance caused by outliers, another new competition mechanism, namely, adaptive learning rate based on samples' energy (ALR), is proposed to update the winner. Data clustering experiments on 2000 simulated datasets comprising clusters of different size and sparsity, as well as with outliers, have been performed to verify the effectiveness of the proposed method. Then we apply EBCL to automatic color image segmentation. Comparison results show that the proposed EBCL outperforms existing competitive learning algorithms.     

Self-organized-expert modular network for classification of spatiotemporal sequences

In this paper, we investigate a form of modular neural network for classification with (a) pre-separated input vectors entering its specialist (expert) networks, (b) specialist networks which are self-organized (radial-basis function or self-targeted feedforward type) and (c) which fuses (or integrates) the specialists with a single-layer net. When the modular architecture is applied to spatiotemporal sequences, the Specialist Nets are recurrent; specifically, we use the Input Recurrent type.The Specialist Networks (SNs) learn to divide their input space into a number of equivalence classes defined by self-organized clustering and learning using the statistical properties of the input domain. Once the specialists have settled in their training, the Fusion Network is trained by any supervised method to map to the semantic classes.We discuss the fact that this architecture and its training is quite distinct from the hierarchical mixture of experts (HME) type as well as from stacked generalization.Because the equivalence classes to which the SNs map the input vectors are determined by the natural clustering of the input data, the SNs learn rapidly and accurately. The fusion network also trains rapidly by reason of its simplicity.We argue, on theoretical grounds, that the accuracy of the system should be positively correlated to the product of the number of equivalence classes for all of the SNs.This network was applied, as an empirical test case, to the classification of melodies presented as direct audio events (temporal sequences) played by a human and subject, therefore, to biological variations. The audio input was divided into two modes: (a) frequency (or pitch) variation and (b) rhythm, both as functions of time. The results and observations show the technique to be very robust and support the theoretical deductions concerning accuracy.     

A novel approach to adaptive relational association rule mining

The paper focuses on the adaptive relational association rule mining problem. Relational association rules represent a particular type of association rules which describe frequent relations that occur between the features characterizing the instances within a data set. We aim at re-mining an object set, previously mined, when the feature set characterizing the objects increases. An adaptive relational association rule method, based on the discovery of interesting relational association rules, is proposed. This method, called ARARM (Adaptive Relational Association Rule Mining) adapts the set of rules that was established by mining the data before the feature set changed, preserving the completeness. We aim to reach the result more efficiently than running the mining algorithm again from scratch on the feature-extended object set. Experiments testing the method's performance on several case studies are also reported. The obtained results highlight the efficiency of the ARARM method and confirm the potential of our proposal.     

An influence-driven feedback system for preference similarity network clustering based consensus group decision making model

Consensus group decision making (CGDM) allows the integration within this area of study of other advanced frameworks such as Social Network Analysis (SNA), Social Influence Network (SIN), clustering and trust-based concepts, among others. These complementary frameworks help to bridge the gap between their corresponding theories in such a way that important elements are not overlooked and are appropriately taken into consideration. In this paper, a new influence-driven feedback mechanism procedure is introduced for a preference similarity network clustering based consensus reaching process. The proposed influence-driven feedback mechanism aims at identifying the network influencer for the generation of advices. This procedure ensures that valuable recommendations are coming from the expert with most similar preferences with the other experts in the group. This is achieved by adapting, from the SIN theory into the CGDM context, an eigenvector-like measure of centrality for the purpose of: (i) measuring the influence score of experts, and (ii) determining the network influencer. Based on the initial evaluations on a set of alternatives provide by the experts in a group, the proposed influence score measure, which is named the σ-centrality, is used to define the similarity social influence network (SSIN) matrix. The σ-centrality is obtained by taking into account both the endogenous (internal network connections) and exogenous (external) factors, which means that SSIN connections as well as the opinion contribution from third parties are permitted in the nomination of the network influencer. The influence-driven feedback mechanism process is designed based on the satisfying of two important conditions to ensure that (1) the revised consensus degree is above the consensus threshold and that (2) the clustering solution is improved.     

Div-clustering: Exploring active users for social collaborative recommendation

Collaborative recommendation (CR) is a popular method of filtering items that may interest social users by referring to the opinions of friends and acquaintances in the network and computer applications. However, CR involves a cold-start problem, in which a newly established recommender system usually exhibits low recommending accuracy because of insufficient data, such as lack of ratings from users. In this study, we rigorously identify active users in social networks, who are likely to share and accept a recommendation in each data cluster to enhance the performance of the recommendation system and solve the cold-start problem. This novel modified CR method called div-clustering is presented to cluster Web entities in which the properties are specified formally in a recommendation framework, with the reusability of the user modeling component considered. We improve the traditional k-means clustering algorithm by applying supplementary works such as compensating for nominal values supported by the knowledge base, as well as computing and updating the k value. We use the data from two different cases to test for accuracy and demonstrate high quality in div-clustering against a baseline CR algorithm. The experimental results of both offline and online evaluations, which also consider in detail the volunteer profiles, indicate that the CR system with div-clustering obtains more accurate results than does the baseline system.     

A hybrid recommender system using artificial neural networks

In the context of recommendation systems, metadata information from reviews written for businesses has rarely been considered in traditional systems developed using content-based and collaborative filtering approaches. Collaborative filtering and content-based filtering are popular memory-based methods for recommending new products to the users but suffer from some limitations and fail to provide effective recommendations in many situations. In this paper, we present a deep learning neural network framework that utilizes reviews in addition to content-based features to generate model based predictions for the business-user combinations. We show that a set of content and collaborative features allows for the development of a neural network model with the goal of minimizing logloss and rating misclassification error using stochastic gradient descent optimization algorithm. We empirically show that the hybrid approach is a very promising solution when compared to standalone memory-based collaborative filtering method.