FindOut: Finding Outliers in Very Large Datasets

Finding the rare instances or the outliers is important in many KDD (knowledge discovery and data-mining) applications, such as detecting credit card fraud or finding irregularities in gene expressions. Signal-processing techniques have been introduced to transform images for enhancement, filtering, restoration, analysis, and reconstruction. In this paper, we present a new method in which we apply signal-processing techniques to solve important problems in data mining. In particular, we introduce a novel deviation (or outlier) detection approach, termed FindOut, based on wavelet transform. The main idea in FindOut is to remove the clusters from the original data and then identify the outliers. Although previous research showed that such techniques may not be effective because of the nature of the clustering, FindOut can successfully identify outliers from large datasets. Experimental results on very large datasets are presented which show the efficiency and effectiveness of the proposed approach. Received 7 September 2000 / Revised 2 February 2001 / Accepted in revised form 31 May 2001 Correspondence and offprint requests to: A. Zhang, Department of Computer Science and Engineering, State University of New York at Buffalo, Buffalo, NY 14260, USA. Email: azhang@cse.buffalo.eduau     

Ranking Pages by Topology and Popularity within Web Sites

We compare two link analysis ranking methods of web pages in a site. The first, called Site Rank, is an adaptation of PageRank to the granularity of a web site and the second, called Popularity Rank, is based on the frequencies of user clicks on the outlinks in a page that are captured by navigation sessions of users through the web site. We ran experiments on artificially created web sites of different sizes and on two real data sets, employing the relative entropy to compare the distributions of the two ranking methods. For the real data sets we also employ a nonparametric measure, called Spearman's footrule, which we use to compare the top-ten web pages ranked by the two methods. Our main result is that the distributions of the Popularity Rank and Site Rank are surprisingly close to each other, implying that the topology of a web site is very instrumental in guiding users through the site. Thus, in practice, the Site Rank provides a reasonable first order approximation of the aggregate behaviour of users within a web site given by the Popularity Rank.     

合理开发利用钨矿资源 促进钨矿业可持续发展

简述了我国钨矿业概况、现有采选技术水平及钨矿加工利用率 ;从资源配置出发 ,阐述了钨矿业发展存在的几个主要问题 ,并且针对性地提出了可持续发展的几点建议。     

Investigations of water inrushes from aquifers under coal seams

In many coal mines, limestone-confined aquifers underlie coal seams. During coal extraction from these mines, water inrushes occur frequently with disastrous consequences. This paper introduces the hydrogeological conditions of the coal mines and the potential water inrush disasters from aquifers under coal seams. It then presents the water inrush mechanism. The main factors which control water inrushes include strata pressure, mining size, geologic structures and the water pressure in the underlying aquifer. Analysis shows that reduction of confinement due to mining is the major cause of the water-conducting failure in the floor strata. The depth of the failure zone is strongly dependent on the mining width. This paper also presents field observation results of the water-conducting failure in the floor strata, and applies the finite element method coupled with stress-dependent permeability to analyze hydraulic conductivity enhancement due to coal extraction. Finally, theoretical and empirical methods to predict water inrushes are given, and technical measures for improving mine design and safety for coal extraction over aquifers are presented. These measures include fault and fracture grouting and mining method modification such as changing long-wall to short-wall mining.     

Using Web resources to construct multilingual medical thesaurus for cross-language medical information retrieval

Due to the language barrier, non-English users are unable to retrieve the most updated medical information from the U.S. authoritative medical websites, such as PubMed and MedlinePlus. However, currently, there is no any cross-language medical information retrieval (CLMIR) system that can help Chinese-speaking consumers cross the language barrier in finding useful English medical information. A few CLMIR systems utilize MeSH (Medical Subject Headings) to help overcome the language barrier. Unfortunately, the traditional Chinese version of MeSH is currently unavailable.In this paper, we employ a semi-automatic term translation method to construct a Chinese–English MeSH by exploiting abundant multilingual Web resources, including Web anchor texts and search–result pages. Through this method, we have developed a Chinese–English Mesh Compilation System to assist knowledge engineers in compiling a Chinese–English medical thesaurus with more than 19,000 entries. Furthermore, this thesaurus has been used to develop a prototypical system for cross-language medical information retrieval, MMODE, which can help consumers retrieve top-quality English medical information using Chinese terms.     

平差法预计矿区导线网精度

分析了矿区导线网精度预计经典方法存在的问题 ,推导出严密平差法精度预计的数学模型 ,并编制机算程序。     

阳泉矿区高产高效矿井建设现状及发展趋势

近年来,阳泉煤业集团公司在高产高效矿井建设方面取得了一定成绩,但与国内外先进水平相比仍有较大差距。介绍了国内外和集团公司高产高效矿井建设的现状,指出了制约集团公司高产高效矿井建设的因素及发展趋势。     

中铝河南分公司矿山在土地复垦中的有益探索

分析了铝土矿露天开采和土地、环境之间的利害关系，并对矿山公司“土地复垦”工艺技术进行了阐述，介绍了中铝河南分公司矿山公司方面的具体作法和成功经验。     

Mining process models with non-free-choice constructs

Process mining aims at extracting information from event logs to capture the business process as it is being executed. Process mining is particularly useful in situations where events are recorded but there is no system enforcing people to work in a particular way. Consider for example a hospital where the diagnosis and treatment activities are recorded in the hospital information system, but where health-care professionals determine the “careflow.” Many process mining approaches have been proposed in recent years. However, in spite of many researchers’ persistent efforts, there are still several challenging problems to be solved. In this paper, we focus on mining non-free-choice constructs, i.e., situations where there is a mixture of choice and synchronization. Although most real-life processes exhibit non-free-choice behavior, existing algorithms are unable to adequately deal with such constructs. Using a Petri-net-based representation, we will show that there are two kinds of causal dependencies between tasks, i.e., explicit and implicit ones. We propose an algorithm that is able to deal with both kinds of dependencies. The algorithm has been implemented in the ProM framework and experimental results shows that the algorithm indeed significantly improves existing process mining techniques.     

A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships

This paper presents a simple, efficient computer-based method for discovering causal relationships from databases that contain observational data. Observational data is passively observed, as contrasted with experimental data. Most of the databases available for data mining are observational. There is great potential for mining such databases to discover causal relationships. We illustrate how observational data can constrain the causal relationships among measured variables, sometimes to the point that we can conclude that one variable is causing another variable. The presentation here is based on a constraint-based approach to causal discovery. A primary purpose of this paper is to present the constraint-based causal discovery method in the simplest possible fashion in order to (1) readily convey the basic ideas that underlie more complex constraint-based causal discovery techniques, and (2) permit interested readers to rapidly program and apply the method to their own databases, as a start toward using more elaborate causal discovery algorithms.