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Data ClassifiCation Algorithms andApplications 2014.英文版

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Data ClassifiCation Algorithms andApplications 2014.英文版

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Data

ClassifiCation

Algorithms and Applications

Chapman & Hall/CRC

Data Mining and Knowledge Discovery Series

PUBLISHED TITLES

SERIES EDITOR

Vipin Kumar

University of Minnesota

Department of Computer Science and Engineering

Minneapolis, Minnesota, U.S.A.

AIMS AND SCOPE

This series aims to capture new developments and applications in data mining and knowledge

discovery, while summarizing the computational tools and techniques useful in data analysis. This

series encourages the integration of mathematical, statistical, and computational methods and

techniques through the publication of a broad range of textbooks, reference works, and hand-

books. The inclusion of concrete examples and applications is highly encouraged. The scope of the

series includes, but is not limited to, titles in the areas of data mining and knowledge discovery

methods and applications, modeling, algorithms, theory and foundations, data and knowledge

visualization, data mining systems and tools, and privacy and security issues.

ADVANCES IN MACHINE LEARNING AND DATA MINING FOR ASTRONOMY

Michael J. Way, Jerey D. Scargle, Kamal M. Ali, and Ashok N. Srivastava

BIOLOGICAL DATA MINING

Jake Y. Chen and Stefano Lonardi

COMPUTATIONAL BUSINESS ANALYTICS

Subrata Das

COMPUTATIONAL INTELLIGENT DATA ANALYSIS FOR SUSTAINABLE

DEVELOPMENT

Ting Yu, Nitesh V. Chawla, and Simeon Simo

COMPUTATIONAL METHODS OF FEATURE SELECTION

Huan Liu and Hiroshi Motoda

CONSTRAINED CLUSTERING: ADVANCES IN ALGORITHMS, THEORY,

AND APPLICATIONS

Sugato Basu, Ian Davidson, and Kiri L. Wagsta

CONTRAST DATA MINING: CONCEPTS, ALGORITHMS, AND APPLICATIONS

Guozhu Dong and James Bailey

DATA CLASSIFICATION: ALGORITHMS AND APPLICATIONS

Charu C. Aggarawal

DATA CLUSTERING: ALGORITHMS AND APPLICATIONS

Charu C. Aggarawal and Chandan K. Reddy

DATA CLUSTERING IN C++: AN OBJECT-ORIENTED APPROACH

Guojun Gan

DATA MINING FOR DESIGN AND MARKETING

Yukio Ohsawa and Katsutoshi Yada

DATA MINING WITH R: LEARNING WITH CASE STUDIES

Luís Torgo

FOUNDATIONS OF PREDICTIVE ANALYTICS

James Wu and Stephen Coggeshall

GEOGRAPHIC DATA MINING AND KNOWLEDGE DISCOVERY,

SECOND EDITION

Harvey J. Miller and Jiawei Han

HANDBOOK OF EDUCATIONAL DATA MINING

Cristóbal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan S.J.d. Baker

INFORMATION DISCOVERY ON ELECTRONIC HEALTH RECORDS

Vagelis Hristidis

INTELLIGENT TECHNOLOGIES FOR WEB APPLICATIONS

Priti Srinivas Sajja and Rajendra Akerkar

INTRODUCTION TO PRIVACY-PRESERVING DATA PUBLISHING: CONCEPTS

AND TECHNIQUES

Benjamin C. M. Fung, Ke Wang, Ada Wai-Chee Fu, and Philip S. Yu

KNOWLEDGE DISCOVERY FOR COUNTERTERRORISM AND

LAW ENFORCEMENT

David Skillicorn

KNOWLEDGE DISCOVERY FROM DATA STREAMS

João Gama

MACHINE LEARNING AND KNOWLEDGE DISCOVERY FOR

ENGINEERING SYSTEMS HEALTH MANAGEMENT

Ashok N. Srivastava and Jiawei Han

MINING SOFTWARE SPECIFICATIONS: METHODOLOGIES AND APPLICATIONS

David Lo, Siau-Cheng Khoo, Jiawei Han, and Chao Liu

MULTIMEDIA DATA MINING: A SYSTEMATIC INTRODUCTION TO

CONCEPTS AND THEORY

Zhongfei Zhang and Ruofei Zhang

MUSIC DATA MINING

Tao Li, Mitsunori Ogihara, and George Tzanetakis

NEXT GENERATION OF DATA MINING

Hillol Kargupta, Jiawei Han, Philip S. Yu, Rajeev Motwani, and Vipin Kumar

RAPIDMINER: DATA MINING USE CASES AND BUSINESS ANALYTICS

APPLICATIONS

Markus Hofmann and Ralf Klinkenberg

RELATIONAL DATA CLUSTERING: MODELS, ALGORITHMS,

AND APPLICATIONS

Bo Long, Zhongfei Zhang, and Philip S. Yu

SERVICE-ORIENTED DISTRIBUTED KNOWLEDGE DISCOVERY

Domenico Talia and Paolo Truno

SPECTRAL FEATURE SELECTION FOR DATA MINING

Zheng Alan Zhao and Huan Liu

STATISTICAL DATA MINING USING SAS APPLICATIONS, SECOND EDITION

George Fernandez

SUPPORT VECTOR MACHINES: OPTIMIZATION BASED THEORY,

ALGORITHMS, AND EXTENSIONS

Naiyang Deng, Yingjie Tian, and Chunhua Zhang

TEMPORAL DATA MINING

Theophano Mitsa

TEXT MINING: CLASSIFICATION, CLUSTERING, AND APPLICATIONS

Ashok N. Srivastava and Mehran Sahami

THE TOP TEN ALGORITHMS IN DATA MINING

Xindong Wu and Vipin Kumar

UNDERSTANDING COMPLEX DATASETS: DATA MINING WITH MATRIX

DECOMPOSITIONS

David Skillicorn

剩余703页未读，继续阅读

wustjk124

2018-08-30

内容清晰，值得学习，全面介绍了数据分类

精简下面表达：Existing protein function prediction methods integrate PPI networks and multivariate bioinformatics data to improve the performance of function prediction. By combining multivariate information, the interactions between proteins become diverse. Different interactions’ functions in functional prediction are various. Combining multiple interactions simply between two proteins can effectively reduce the effect of false negatives and increase the number of predicted functions, but it can also increase the number of false positive functions, which contribute to nonobvious enhancement for the overall functional prediction performance. In this article, we have presented a framework for protein function prediction algorithms based on PPI network and semantic similarity with the addition of protein hierarchical functions to them. The framework relies on diverse clustering algorithms and the calculation of protein semantic similarity for protein function prediction. Classification and similarity calculations for protein pairs clustered by the functional feature are more accurate and reliable, allowing for the prediction of protein function at different functional levels from different proteomes, and giving biological applications greater flexibility.The method proposed in this paper performs well on protein data from wine yeast cells, but how well it matches other data remains to be verified. Yet until now, most unknown proteins have only been able to predict protein function by calculating similarities to their homologues. The predictions result of those unknown proteins without homologues are unstable because they are relatively isolated in the protein interaction network. It is difficult to find one protein with high similarity. In the framework proposed in this article, the number of features selected after clustering and the number of protein features selected for each functional layer has a significant impact on the accuracy of subsequent functional predictions. Therefore, when making feature selection, it is necessary to select as many functional features as possible that are important for the whole interaction network. When an incorrect feature was selected, the prediction results will be somewhat different from the actual function. Thus as a whole, the method proposed in this article has improved the accuracy of protein function prediction based on the PPI network method to a certain extent and reduces the probability of false positive prediction results.

本文提出了一种基于PPI网络和语义相似性，加上蛋白质分层功能的蛋白质功能预测算法框架，对酒葡萄酵母细胞的蛋白质数据表现出良好的效果，但其他数据的效果如何仍有待验证。此外，该框架中的功能特征选择的数量以及每个功能层中蛋白质特征的选择量，对于后续功能预测的准确度有很大影响。因此，在进行特征选择时，必须尽可能选择对整个交互网络重要的多种功能特征，从而提高蛋白质功能预测的准确性，减少错误预测的概率。

担任中国联通网络AI中心资深算法架构师岗位，负责管理中心算法与大数据团队。工作内容包括以下三个方面，一是算法平台算法库建设，为通信业务场景提供基于时序预测、异常检测、图像识别、文本分析、通用分类预测等丰富的算法模型库及大数据特征数仓。二是建设基于知识图谱的通信网络拓扑，通过图算法模型构建智能排障、智能网络资源核配、ChatOPS等应用。三是针对通信领域网络移动网络、宽带等业务场景，基于人工智能技术进行科技攻关，在计算机视觉，时序异常检测、信号定位、流量分析等领域，形成基于智能巡检、自动化异常检测等算法能力，并在实际业务场景落地。主要业绩如下：一、形成基于深度学习DeepAR、Nbeats、Dlinear等复杂神经网络的时序预测算子库，通用通信网络KPI时序预测精度MAPE维持在20%以内，月调用量10亿以上；用户画像标签模型完成统一大模型建设，F1-Score在0.4以上，月度调用量2亿；二、完成多省网络资源、告警信息知识图谱构建，智能故障定位召回率95%以上，Top3故障准确率80%以上。三、智能巡检系统上线，具备OCR识别、传统仪表读数、设备检测等多项能力，平均ACC维持在90%以上。按照专业的简历介绍语言风格，润色一下，使用中文，并且纠正下语法错误及用词错误。

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< >用户视图模式权限稍低 [ ]系统视图模式权限高 <>system-view 切换到系统视图 [ ]sysname R1命名 [ ]quit 退出当前模式 ?提示信息命令支持简写 tab 键补全命令 <>language-mode Chinese 提示语言改为中文 dis current-configuration 显示当前所有配置 more：回车键翻一行，空格翻一页其他任意键退出 c

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