嵌入式数据挖掘系统实现方法研究

文中分析了数据挖掘系统与数据库及数据仓库系统间的关系,提出了嵌入式数据挖掘系统的思想、实现方法,及其关键技术。并引用决策树算法对嵌入式数据挖掘系统的实现方法做了描述。文中对嵌入式数据挖掘系统的未来进行了展望,一旦数据挖掘过程变得简单易用,并且能够无缝地集成到商业过程和企业的通用数据和信息流中,将会有新的应用和协同作用使数据挖掘成为功能完备的数据仓库中一项重要的要求。     

基于聚类、粗糙集和支持向量机的故障诊断

故障诊断有多种方法,利用支持向量机进行故障诊断是其中一种比较有效的方法。但是,故障征兆往往有多个,很难确定哪些征兆是关键的,哪些征兆是冗余的,这样就要搜集处理大量的数据,使支持向量机的结构变得复杂,诊断效率不高。为了提高故障诊断的效率,文中提出一种将K均值聚类、粗糙集、支持向量机相结合进行故障诊断的方法。这种方法首先利用K均值聚类对数据进行预处理,然后利用粗糙集对属性进行约简,最后再用支持向量机进行故障诊断。这样可以充分发挥粗糙集与支持向量机各自的优势,实例证明它可以提高故障诊断的速度和精确度,是一种较好的故障诊断方法。     

Unsupervised image segmentation using a simple MRF model with a new implementation scheme

A simple Markov random field model with a new implementation scheme is proposed for unsupervised image segmentation based on image features. The traditional two-component MRF model for segmentation requires training data to estimate necessary model parameters and is thus unsuitable for unsupervised segmentation. The new implementation scheme solves this problem by introducing a function-based weighting parameter between the two components. Using this method, the simple MRF model is able to automatically estimate model parameters and produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to segment various types of images (gray scale, color, texture) and achieves an improvement over the traditional method.     

Hypergraph Models and Algorithms for Data-Pattern-Based Clustering

In traditional approaches for clustering market basket type data, relations among transactions are modeled according to the items occurring in these transactions. However, an individual item might induce different relations in different contexts. Since such contexts might be captured by interesting patterns in the overall data, we represent each transaction as a set of patterns through modifying the conventional pattern semantics. By clustering the patterns in the dataset, we infer a clustering of the transactions represented this way. For this, we propose a novel hypergraph model to represent the relations among the patterns. Instead of a local measure that depends only on common items among patterns, we propose a global measure that is based on the cooccurences of these patterns in the overall data. The success of existing hypergraph partitioning based algorithms in other domains depends on sparsity of the hypergraph and explicit objective metrics. For this, we propose a two-phase clustering approach for the above hypergraph, which is expected to be dense. In the first phase, the vertices of the hypergraph are merged in a multilevel algorithm to obtain large number of high quality clusters. Here, we propose new quality metrics for merging decisions in hypergraph clustering specifically for this domain. In order to enable the use of existing metrics in the second phase, we introduce a vertex-to-cluster affinity concept to devise a method for constructing a sparse hypergraph based on the obtained clustering. The experiments we have performed show the effectiveness of the proposed framework.     

Space-Time Hierarchical Radiosity with Clustering and Higher-Order Wavelets

We address in this paper the issue of computing diffuse global illumination solutions for animation sequences. The principal difficulties lie in the computational complexity of global illumination, emphasized by the movement of objects and the large number of frames to compute, as well as the potential for creating temporal discontinuities in the illumination, a particularly noticeable artifact. We demonstrate how space‐time hierarchical radiosity, i.e. the application to the time dimension of a hierarchical decomposition algorithm, can be effectively used to obtain smooth animations: first by proposing the integration of spatial clustering in a space‐time hierarchy; second, by using a higher‐order wavelet basis adapted for the temporal dimension. The resulting algorithm is capable of creating time‐dependent radiosity solutions efficiently.     

Comparing spatial clustering tests based on rare to common spatial events

This paper compares the performance of three clustering tests––Rogerson R, Getis-Ord G and Lin-Zeng LR-T––using a range of simulated sample distributions from rare to common spatial events. It is shown that all of the tests are sensitive to high value clustering, and all but G are sensitive to low-value clustering. For a spatial pattern exhibiting negative spatial autocorrelation, R is likely to associate the autocorrelation with clustering when sample size is greater than 20, while LR-T and G are unlikely to accept any presence of negative autocorrelation as clustering.     

Interval Set Clustering of Web Users with Rough K-Means

Data collection and analysis in web mining faces certain unique challenges. Due to a variety of reasons inherent in web browsing and web logging, the likelihood of bad or incomplete data is higher than conventional applications. The analytical techniques in web mining need to accommodate such data. Fuzzy and rough sets provide the ability to deal with incomplete and approximate information. Fuzzy set theory has been shown to be useful in three important aspects of web and data mining, namely clustering, association, and sequential analysis. There is increasing interest in research on clustering based on rough set theory. Clustering is an important part of web mining that involves finding natural groupings of web resources or web users. Researchers have pointed out some important differences between clustering in conventional applications and clustering in web mining. For example, the clusters and associations in web mining do not necessarily have crisp boundaries. As a result, researchers have studied the possibility of using fuzzy sets in web mining clustering applications. Recent attempts have used genetic algorithms based on rough set theory for clustering. However, the genetic algorithms based clustering may not be able to handle the large amount of data typical in a web mining application. This paper proposes a variation of the K-means clustering algorithm based on properties of rough sets. The proposed algorithm represents clusters as interval or rough sets. The paper also describes the design of an experiment including data collection and the clustering process. The experiment is used to create interval set representations of clusters of web visitors.     

Compiling CSPs into Tree-Driven Automata for Interactive Solving

Constraint programming techniques are widely used to model and solve decision problems and many algorithms have been developed to solve automatically and efficiently families of CSPs; nevertheless, they do not help solve interactive decision support problems, like product configuration. In such problems, the user chooses the values of the variables, and the role of the system is not to solve the CSP, but to help the user in this task. Dynamic global consistency maintaining is one of the most useful functionalities that should be offered by such a CSP platform. Unfortunately, this task is intractable in the worst case. Since interactivity requires short response times, intractability must be circumvented some way. To this end, compilation methods have been proposed that transform the original problem into a data structure allowing a short response time. In this paper, we extend the work of Amilhastre et al. [1] and Vempaty [15] by the use of a new structure, tree-driven automata, that takes advantage of the structural characteristics of configuration problems (decomposition of the components into independent subcomponents). Tree-driven automata can be far more compact than classical automata while keeping their good properties, especially a tractable complexity for the maintenance of global consistency.     

电厂锅炉水冷壁高温腐蚀程度判别技术研究

燃煤电厂锅炉水冷壁高温腐蚀危害性大，形成原因复杂，腐蚀程度的倾向难以判别。文中介绍运用灰，色系统理论开展水冷壁高温腐蚀倾向判别研究的成果，将高温腐蚀原因综合概括为标煤折算硫分、壁面附近CO含量，当量切圆直径、管壁温度四大影响因素，基于灰色聚类法获得了可定量判别水冷壁高温腐蚀程度的的技术方法。     

基于模糊聚类和最小二乘回归的罩式退火炉冷却时间预报及其应用

按照退火计划内各钢卷的钢种和厚度对退火中的冷却工艺进行分类 ,将罩式退火炉冷却时间预报的多入单出建模问题化为一组单入单出建模问题。采用模糊C均值聚类方法对退火生产数据进行处理 ,再基于得到的聚类数据点进行指数最小二乘回归。现场应用表明 ,该方法能够较为准确地实现退火炉冷却时间预报 ,满足了现场要求。