Research on Outlier Mining and Intensional Knowledge Discovery

数据是当今信息社会宝贵的资源，发现并利用潜藏在那些复杂数据集中的有用知识已成为科学决策的前提。数据挖掘技术通过关联规则、聚类及分类等方法实现从大量的、不完全的、有噪声的数据中，提取隐含在其中的、人们事先不知道的、潜在有用的信息和知识。 离群挖掘是数据挖掘中的一项重要技术。所谓离群点就是那些明显偏离其它数据、不满足数据的一般模式或行为，以至于被怀疑可能是由另外一种完全不同的机制产生的数据对象。离群数据不等同于错误数据，有的离群数据可能蕴含着重要的信息。如欺诈行为、入侵行为、不寻常的消费行为等。因此，对离群数据进行研究具有十分重要的意义。 离群挖掘可分为3个子问题：①在给定的数据集合中什么样的...Data are considered as a kind of most valuable resource in information society today. Lots of useful knowledge is hidden in complex datasets, discovering and using such knowledge have become the preconditions of scientific decision. Data mining extract the potential useful information and knowledge which is hidden and prior ignorant from large, uncompleted and noisy datasets by means of associatio...学位：工学硕士院系专业：信息科学与技术学院计算机科学系_计算机软件与理论学号：2005130232

An Improved Algor ithm of K- means

摘要: K- means 算法是最常用的聚类算法之一, 有很多的优点, 但也存在着不足。它不仅对样本的输入顺序敏感, 可能产 生局部最优解, 而且受孤立点的影响很大。文章正是针对这些不足, 提出了一种改进的K- means 算法, 主要从数据预处 理、初始聚类中心的选择方面进行了改进, 并做了改进前后算法的对比实验。结果表明, 改进后的算法不但更具稳定性, 准 确度也高, 受孤立点的影响也大大降低。英文摘要：Abstr act:K- means algorithm is one of the most widespread methods in clustering, including both strong points and also shortages. Not only is it sensitive to the order of sample data, but also it may make out the local excellent and be affected by the outliers. Given these shortages, an improved algorithm is discussed, which makes improvements in data preprocessing and selection of original clustering center. Check experiment was done, which indicates the improved one is more stable, more accurate and the affection by the outliers is down to a much low figure

An Improved Algorithm of K-means

K-means算法是最常用的聚类算法之一,有很多的优点,但也存在着不足。它不仅对样本的输入顺序敏感,可能产生局部最优解,而且受孤立点的影响很大。文章正是针对这些不足,提出了一种改进的K-means算法,主要从数据预处理、初始聚类中心的选择方面进行了改进,并做了改进前后算法的对比实验。结果表明,改进后的算法不但更具稳定性,准确度也高,受孤立点的影响也大大降低。K-means algorithm is one of the most widespread methods in clustering,including both strong points and also shortages.Not only is it sensitive to the order of sample data,but also it may make out the local excellent and be affected by the outliers.Given these shortages,an improved algorithm is discussed,which makes improvements in data preprocessing and selection of original clustering center.Check experiment was done,which indicates the improved one is more stable,more accurate and the affection by the outliers is down to a much low figure

173例次万古霉素血药浓度监测与临床应用分析

目的:分析某院万古霉素（VAN）血药浓度监测结果,为临床合理应用提供依据。方法:采用酶放大免疫分析法监测VAN血药浓度,并对该院2013-2017年113例患者173例次VAN血药浓度监测结果进行回顾性总结分析。结果:VAN血药质量浓度均值为（17.7±14.2）μg·mL-1,在治疗窗范围内(1020μg·mL-1)的仅占31.79%,其中首次监测在治疗窗范围内的仅占28.57%,达到中毒浓度(>20μg·mL-1)的患者占32.95%;>60岁组患者进行血药浓度监测的比例最大（38.73%）,且血药浓度均值（23.9±17.4）μg·mL-1超出治疗窗范围;日剂量60岁患者血药浓度均值都达到中毒浓度;肾功能异常患者与肾功能正常患者血药谷浓度有显著性差异。结论:VAN血药浓度个体差异较大,该院VAN血药浓度在治疗窗内的比例较低,需加强血药浓度监测;临床医生和药师需重视血药浓度结果与临床应用相结合,及时调整给药方案,实现个体化给药。漳州市自然科学基金课题(编号:ZZ2017J41);;青年苗圃(编号:17Y023

Measurement of integrated luminosity of data collected at 3.773 GeV by BESIII from 2021 to 2024

We present a measurement of the integrated luminosity e+e- of collision data collected by the BESIII detector at the BEPCII collider at a center-of-mass energy of Ecm = 3.773 GeV. The integrated luminosities of the datasets taken from December 2021 to June 2022, from November 2022 to June 2023, and from October 2023 to February 2024 were determined to be 4.995±0.019 fb-1, 8.157±0.031 fb-1, and 4.191±0.016 fb-1, respectively, by analyzing large angle Bhabha scattering events. The uncertainties are dominated by systematic effects, and the statistical uncertainties are negligible. Our results provide essential input for future analyses and precision measurements

Determination of the number of ψ(3686) events taken at BESIII

The number of ψ(3686) events collected by the BESIII detector during the 2021 run period is determined to be (2259.3±11.1)×106 by counting inclusive ψ(3686) hadronic events. The uncertainty is systematic and the statistical uncertainty is negligible. Meanwhile, the numbers of ψ(3686) events collected during the 2009 and 2012 run periods are updated to be (107.7±0.6)×106 and (345.4±2.6)×106, respectively. Both numbers are consistent with the previous measurements within one standard deviation. The total number of ψ(3686) events in the three data samples is (2712.4±14.3)×10^