Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery最新文献_第9页

A comprehensive review on updating concept lattices and its application in updating association rules 概念格更新及其在关联规则更新中的应用综述

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-05 DOI: 10.1002/widm.1401

Ebtesam E. Shemis, Ammar Mohammed

{"title":"A comprehensive review on updating concept lattices and its application in updating association rules","authors":"Ebtesam E. Shemis, Ammar Mohammed","doi":"10.1002/widm.1401","DOIUrl":"https://doi.org/10.1002/widm.1401","url":null,"abstract":"Formal concept analysis (FCA) visualizes formal concepts in terms of a concept lattice. Usually, it is an NP‐problem and consumes plenty of time and storage space to update the changes of the lattice. Thus, introducing an efficient way to update and maintain such lattices is a significant area of interest within the field of FCA and its applications. One of those vital FCA applications is the association rule mining (ARM), which aims at generating a loss‐less nonredundant compact Association Rule‐basis (AR‐basis). Currently, the real‐world data rapidly overgrow that asks the need for updating the existing concept lattice and AR‐basis upon data change continually. Intuitively, updating and maintaining an existing concept‐lattice or AR‐basis is much more efficient and consistent than reconstructing them from scratch, particularly in the case of massive data. So far, the area of updating both concept lattice and AR‐basis has not received much attention. Besides, few noncomprehensive studies have focused only on updating the concept lattice. From this point, this article comprehensively introduces basic knowledge regarding updating both concept lattices and AR‐basis with new illustrations, formalization, and examples. Also, the article reviews and compares recent remarkable works and explores the emerging future research trends.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"211 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76052101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

Mining the online infosphere: A survey 挖掘在线信息圈:一项调查

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-02 DOI: 10.1002/widm.1453

Sayantan Adak, Souvic Chakraborty, Paramtia Das, Mithun Das, A. Dash, Rima Hazra, Binny Mathew, Punyajoy Saha, Soumya Sarkar, Animesh Mukherjee

{"title":"Mining the online infosphere: A survey","authors":"Sayantan Adak, Souvic Chakraborty, Paramtia Das, Mithun Das, A. Dash, Rima Hazra, Binny Mathew, Punyajoy Saha, Soumya Sarkar, Animesh Mukherjee","doi":"10.1002/widm.1453","DOIUrl":"https://doi.org/10.1002/widm.1453","url":null,"abstract":"The evolution of Artificial Intelligence (AI)‐based systems and applications have pervaded everyday life to make decisions that have a momentous impact on individuals and society. With the staggering growth of online data, often termed as the online infosphere, it has become paramount to monitor the infosphere to ensure social good as AI‐based decisions are severely dependent. This survey aims to provide a comprehensive review of some of the most important research areas related to the infosphere, focusing on the technical challenges and potential solutions. The survey also outlines some of the important future directions. We begin by focussing on the collaborative systems that have emerged within the infosphere with a special thrust on Wikipedia. In the follow‐up, we demonstrate how the infosphere has been instrumental in the growth of scientific citations and collaborations, thus fuelling interdisciplinary research. Finally, we illustrate the issues related to the governance of the infosphere, such as the tackling of the (a) rising hateful and abusive behavior and (b) bias and discrimination in different online platforms and news reporting.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"9 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88462129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Investigation of PM10 prediction utilizing data mining techniques: Analyze by topic 利用数据挖掘技术预测PM10的研究:按主题分析

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1423

Krittakom Srijiranon, Narissara Eiamkanitchat, Sakgasit Ramingwong, K. Cosh, L. Ramingwong

引用次数: 1

Introduction to neural network‐based question answering over knowledge graphs 介绍基于神经网络的知识图谱问答

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1389

Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer

引用次数: 24

Review on publicly available datasets for educational data mining 教育数据挖掘的公开可用数据集综述

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1403

M. Mihăescu, Paul-Stefan Popescu

{"title":"Review on publicly available datasets for educational data mining","authors":"M. Mihăescu, Paul-Stefan Popescu","doi":"10.1002/widm.1403","DOIUrl":"https://doi.org/10.1002/widm.1403","url":null,"abstract":"The availability of a dataset represents a critical component in educational data mining (EDM) pipelines. Once the dataset is at hand, the next steps within the research methodology regard proper research issue formulation, data analysis pipeline design and implementation and, finally, presentation of validation results. As the EDM research area is continuously growing due to the increasing number of available tools and technologies, one of the critical issues that constitute a bottleneck regards a properly documented review on publicly available datasets. This paper aims to present a succinct, yet informative, description of the most used publicly available data sources along with their associated EDM tasks, used algorithms, experimental results and main findings. We have found that there are three types of data sources: well‐known data sources, datasets used in EDM competitions and standalone EDM datasets. We conclude that the success of the future of EDM data sources will rely on their ability to manage proposed approaches and their experimental results as a dashboard of benchmarked runs. Under these circumstances, the reproducibility of data analysis pipelines and benchmarking of proposed algorithms becomes at hand for the research community such that progress in the EDM domain may be much more easily acquired. The most crucial outcome regards the possibility of continuously improving existing data analysis pipelines by tackling EDM tasks that rely on publicly available datasets and benchmarking data analysis pipelines that use open‐source implementations.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"10 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75059923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Dynamical algorithms for data mining and machine learning over dynamic graphs 动态图上的数据挖掘和机器学习的动态算法

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1393

Mostafa Haghir Chehreghani

引用次数: 0

Data stream analysis: Foundations, major tasks and tools 数据流分析:基础、主要任务和工具

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1405

M. Bahri, A. Bifet, J. Gama, Heitor Murilo Gomes, S. Maniu

引用次数: 56

Fuzzy rough sets and fuzzy rough neural networks for feature selection: A review 模糊粗糙集与模糊粗糙神经网络特征选择研究进展

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-01-01 DOI: 10.1002/widm.1402

Wanting Ji, Y. Pang, Xiaoyun Jia, Zhongwei Wang, Feng Hou, Baoyan Song, Mingzhe Liu, Ruili Wang

引用次数: 29

Privacy preserving classification over differentially private data 对不同的私有数据进行隐私保护分类

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2020-12-13 DOI: 10.1002/widm.1399

Ezgi Zorarpacı, S. A. Özel

{"title":"Privacy preserving classification over differentially private data","authors":"Ezgi Zorarpacı, S. A. Özel","doi":"10.1002/widm.1399","DOIUrl":"https://doi.org/10.1002/widm.1399","url":null,"abstract":"Privacy preserving data classification is an important research area in data mining field. The goal of a privacy preserving classification algorithm is to protect the sensitive information as much as possible, while providing satisfactory classification accuracy. Differential privacy is a strong privacy guarantee that enables privacy of sensitive data stored in a database by determining the ratio of sensitive information leakage with respect to an ɛ parameter. In this study, our aim is to investigate the classification performance of the state‐of‐the‐art classification algorithms such as C4.5, Naïve Bayes, One Rule, Bayesian Networks, PART, Ripper, K*, IBk, and Random tree for performing privacy preserving classification. To preserve privacy of the data to be classified, we applied input perturbation technique coming from differential privacy, and observed the relationship between the ɛ parameter values and accuracy of the classifiers. To our best knowledge, this article is the first study that analyzes the performances of the well‐known classification algorithms over differentially private data, and discovers which datasets are more suitable for privacy preserving classification when input perturbation is applied to provide data privacy. The classification algorithms are compared by using the differentially private versions of the well‐known datasets from the UCI repository. According to the experimental results, we observed that, as ɛ parameter value increases, better classification accuracies are achieved with lower privacy levels. When the classifiers are compared, Naïve Bayes classifier is the most successful method. The ɛ parameter should be greater than or equal to 2 (i.e., ɛ ≥2) to achieve cloud server is malicious and untrusted, sensitive data will satisfactory classification accuracies.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"60 1","pages":""},"PeriodicalIF":7.8,"publicationDate":"2020-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82716824","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

Predicting the ratings of Amazon products using Big Data 利用大数据预测亚马逊产品的评级

IF 7.8 2区计算机科学

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2020-12-12 DOI: 10.1002/widm.1400

Jongwook Woo, Monika Mishra

引用次数: 7