基于WEB文本数据挖掘的研究

万维网是一个巨大的、分布广泛和全球性的信息服务中心,它涉及新闻、广告、消费信息、金融管理、教育、政府、电子商务和许多其他信息服务。Web文本挖掘系统是挖掘技术的重要应用方向,它是指在给定的分类体系下,根据网页的内容自动判别内容类别的过程。     

基于属性加权的改进K-Means算法

经典的K-Means算法认为被分析样本的各个属性对聚类结果的贡献均匀,没有考虑不同属性特征对聚类结果可能造成的不同影响。文章提出了一种基于样本属性加权的K-Means算法。该算法利用变异系数赋权法对属性进行加权处理,通过权值反映各个属性对聚类结果的贡献的大小。实验表明,该算法在不改变时间、空间复杂度的情况下能取得更好的聚类结果。     

Self-organizing map network as an interactive clustering tool — An application to group technology

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to group technology. A computer program for implementing the SOM neural networks is developed and the results are compared with other clustering approaches used in group technology. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for part family formation in group technology.     

New method of mining incomplete data

The data used in the process of knowledge discovery often includes noise and incomplete information. The boundaries of different classes of these data are blur and unobvious. When these data are clustered or classified, we often get the coverings instead of the partitions, and it usually makes our information system insecure. In this paper, optimal partitioning of incomplete data is researched. Firstly, the relationship of set cover and set partition is discussed, and the distance between set cover and set partition is defined. Secondly, the optimal partitioning of given cover is researched by the combing and parting method, acquiring the optimal partition from three different partitions set family is discussed. Finally, the corresponding optimal algorithm is given. The real wireless signals offten contain a lot of noise, and there are many errors in boundaries when these data is clustered based on the tradional method. In our experimant, the proposed method improves correct rate greatly, and the experimental results demonstrate the method’s validity.     

抽样改进加权核大数据谱聚类算法

经典谱聚类算法将数据聚类转为图划分问题,在分析其Normalized Cut函数与传统加权核k-means等价基础上,设计了一种基于抽样改进加权核k-means算法的大规模数据集谱聚类算法,算法通过加权核k-means迭代优化避免Laplacian矩阵特征分解的大量资源占用,通过随机映射得到近似奇异值分解,并由近似奇异...     

梅钢运输调度系统架构分析

介绍了梅钢运输调度系统的软硬件架构体系及其在梅钢ERP系统的定位问题，并根据该系统的运行性能指标分析，提出了对网络架构的改进设想。     

A cluster based mobility prediction scheme for ad hoc networks

The ad hoc networks are completely autonomous wireless networks where all the users are mobile. These networks do not work on any infrastructure and the mobiles communicate either directly or via other nodes of the network by establishing routes. These routes are prone to frequent ruptures because of nodes mobility. If the future movement of the mobile can be predicted in a precise way, the resources reservation can be made before be asked, which enables the network to provide a better QoS. In this aim, we propose a virtual dynamic topology, which on one hand, will organize the network as well as possible and decreases the impact of mobility, and on the other hand, is oriented user mobility prediction. Our prediction scheme uses the evidence theory of Dempster–Shafer in order to predict the future position of the mobile by basing itself on relevant criteria. These ones are related to mobility and network operation optimisation. The proposed scheme is flexible and can be extended to a general framework. To show the relevance of our scheme, we combine it with a routing protocol. Then, we implemented the prediction-oriented topology and the prediction scheme which performs on it. We implemented also a mobility prediction based routing protocol. Simulations are made according to a set of elaborate scenarios.     

Application of a New Fuzzy Clustering Algorithm in Intrusion Detection

This paper presents a new Section Set Adaptive FCM algorithm. The algorithm solved the shortcomings of localoptimality, unsure classification and clustering numbers ascertained previously. And it improved on the architecture of FCM al-gorithm, enhanced the analysis for effective clustering. During the clustering processing, it may adjust clustering numbers dy-namically. Finally, it used the method of section set decreasing the time of classification. By experiments, the algorithm can im-prove dependability of clustering and correctness of classification.     

模糊聚类在城市环境噪声区划中的应用

通过对城市声学环境定量区划方法的研究,提出使用模糊聚类模型进行区划,该方法简单,误差小,具有较强的实用价值.