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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Wu, Qiuyang Zhang, Liang Xu, Jingxuan Xu, Run Information Communication Branch of State Grid Anhui Electric Power Co. Ltd. Anhui Hefei230022 China

ISBN: (纸本)9798350383348

With the advent of big data era, data mining algorithms have been widely used in various fields. However, in the process of data processing, these algorithms may also have some loopholes, which lead to deviation or error of results. Therefore, it is very important to verify the vulnerability of data mining algorithm for improving the accuracy and reliability of the algorithm. Vulnerability verification of data mining algorithm is an important means to ensure the accuracy and reliability of the algorithm. Through comparative experiments, anomaly detection, fuzzy testing, cross-validation and interpretability checking, we can find and solve the loopholes in the algorithm. © 2024 IEEE.

关键词： computer technology data mining algorithms framework design and implementation vulnerability verification

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Application of Sentiment Analysis Algorithm based on Big data in Mental Health Assessment 3

Application of Sentiment Analysis Algorithm based on Big Dat...

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3rd International Conference on data Analytics, Computing and Artificial Intelligence, ICDACAI 2024

作者： Deng, Qiyin Chengdu Agricultural College Chengdu City611130 China

ISBN: (纸本)9798331519032

Nowadays, college students are under increasing pressure. The accumulation of various pressures not only brings great harm to human body, but also brings many mental health (MH) problems. In this paper, large-scale Internet big data is taken as the research object, and the data mining algorithm based on heuristic clustering is studied. After text clustering, an evaluation index system based on text set is constructed based on the corresponding index of text set. The first-level evaluation indicators include emotional adjustment, learning influence, life influence and career influence. Using this model to evaluate the MH of college students can cluster the MH indicators of college students well in a large number of data, and the false positive rate and the false negative rate will not exceed 1%. This paper is helpful to the harmonious development of college students' MH. © 2024 IEEE.

关键词： data mining algorithms emotional regulation evaluation models learning impact psychological health of college students

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Financial Risk Early Warning Based on Big data Analysis 1

Financial Risk Early Warning Based on Big Data Analysis

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1st IEEE International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023

作者： Wan, Lanlan School of Economics and Management Shanghai University of Political Science and Law China

ISBN: (纸本)9798350316469

This article proposes a financial risk warning method based on big data analysis, aiming to discover and predict potential financial risks in a timely manner by analyzing and mining a large amount of financial data. This method utilizes advanced big data analysis techniques internal and external information of financial institutions, in order to identify key factors and trends that may trigger risks. Through experimental verification, this method can effectively alert financial risks, improve the risk management level and risk prevention ability of financial institutions. Experiments have also shown that the financial risk warning capability based on big data can be improved by 95%. © 2023 IEEE.

关键词： big data data mining algorithms financial crisis Warning level

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Improving Rule mining for Entry, Descent, and Landing Simulations Using Knowledge Graphs

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2022年第9期19卷 598-612页

作者： De Leon, Samalis Santini Selva, Daniel Way, David W. Texas A&M Univ Dept Aerosp Engn College Stn TX 77843 USA NASA Langley Res Ctr Atmospher Flight & Entry Syst Branch D205 Hampton VA 23666 USA

Designing planetary entry, descent, and landing (EDL) systems requires analyzing large datasets containing tens of thousands of parameters. These datasets are generally manually analyzed by subject-matter experts trying to find interesting correlations and couplings between parameters that explain the behaviors observed. A popular approach to automate the extraction of explanation rules is association rule mining, in which rules with high statistical strength are mined from the dataset. However, current rule mining algorithms generate too many rules that are redundant, too complex, too obvious, or do not make sense to the user. In this paper, we propose a new approach to improve the comprehensibility, insightfulness, and usefulness of the association rules generated during the analysis of an EDL dataset by leveraging a user-provided knowledge graph. The knowledge graph captures the user knowledge about EDL and the specific problem at hand. We then use a statistical relational learning framework based on probabilistic soft logic to assess the degree of consistency of the rule with the user's knowledge of the system. The method is validated in a small study with N=6 subject-matter experts. The results of the study also show interesting relationships between comprehensibility, usefulness, and insightfulness of the extracted rules.

关键词： Model Based System Engineering Probability Density Functions Flight Path Angle data mining algorithms Dynamic Pressure Aerodynamics Monte Carlo Simulation Terrain Relative Navigation Inertial Measurement Unit Artificial Intelligence

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Generalized association rule mining with constraints

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INFORMATION SCIENCES 2012年 194卷 68-84页

作者： Baralis, Elena Cagliero, Luca Cerquitelli, Tania Garza, Paolo Politecn Milan Dipartimento Elettron & Informat I-20133 Milan Italy Politecn Torino Dipartimento Automat & Informat I-10129 Turin Italy

Generalized association rule extraction is a powerful tool to discover a high level view of the interesting patterns hidden in the analyzed data. However, since the patterns are extracted at any level of abstraction, the mined rule set may be too large to be effectively exploited in the decision making process. Thus, to discover valuable and interesting knowledge a post-processing step is usually required. This paper presents the CoGAR framework to efficiently support constrained generalized association rule mining. The generalization process of CoGAR exploits a (user-provided) multiple-taxonomy to drive an opportunistic itemset generalization process, which prevents discarding relevant but infrequent knowledge by aggregating features at different granularity levels. Besides the traditional support and confidence constraints, two further constraints are enforced: (i) schema constraints and (ii) the opportunistic confidence constraint. Schema constraints allow the analyst to specify the structure of the patterns of interest and drive the itemset mining phase. The opportunistic confidence constraint, a new constraint proposed in this paper, allows us to discriminate between significant and redundant rules by analyzing similar rules belonging to different abstraction levels. This constraint is enforced during the rule generation step. Experiments performed on real datasets collected in two different application domains show the effectiveness and the efficiency of the proposed framework in mining constrained generalized association rules. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Generalized association rules data mining algorithms Knowledge discovery Context-aware mining Network traffic analysis mining with constraints

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Performance evaluation of mathematical predictive modeling for air quality forecasting

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2019年第5-Sup期22卷 12481-12493页

作者： Selvi, S. Chandrasekaran, M. Govt Coll Engn Dept Comp Sci & Engn Bargur Tamil Nadu India Govt Coll Engn Dept Elect & Commun Engn Bargur Tamil Nadu India

The extensive data has got more attention among researchers in the field of data analytics. The rate of growth of information in the area of information sciences exceeds to a more significant extent. And now it's a significant challenge to deal this big data such as difficulties in data analysis, data capture, data storage and data visualization, etc. This paper illustrates to mine knowledge derived from the environmental issues. It is proposed to utilize the study of several processes of effects such as mitigation, various arrangements aimed at risk distribution. This paper briefs as to how to use data mining algorithms which are built on R programming tool. There are R packages open-air and ropenaq which had been developed for analyzing air pollution environment data. It is shown that how this package can effectively makes utilization of environmental data sets to derive patterns. These derived patterns can be then applied to study the environmental issues which are helpful to develop a predictive model. And this predictive model plays a vital role in decision-making which involves uncertainty. Hence, scientific representations take to develop the main feature of decision-making care in many procedure measures, especially those for some precautions from natural disasters.

关键词： data mining algorithms Rough set theory R programming tool Big data and machine learning

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Knowledge discovery in medicine: Current issue and future trend

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第9期41卷 4434-4463页

作者： Esfandiari, Nura Babavalian, Mohammad Reza Moghadam, Amir-Masoud Eftekhari Tabar, Vahid Kashani Islamic Azad Univ Fac Comp & Informat Technol Qazvin Branch Qazvin Iran Kashan Univ Med Sci Trauma Res Ctr Kashan Iran

data mining is a powerful method to extract knowledge from data. Raw data faces various challenges that make traditional method improper for knowledge extraction. data mining is supposed to be able to handle various data types in all formats. Relevance of this paper is emphasized by the fact that data mining is an object of research in different areas. In this paper, we review previous works in the context of knowledge extraction from medical data. The main idea in this paper is to describe key papers and provide some guidelines to help medical practitioners. Medical data mining is a multidisciplinary field with contribution of medicine and data mining. Due to this fact, previous works should be classified to cover all users' requirements from various fields. Because of this, we have studied papers with the aim of extracting knowledge from structural medical data published between 1999 and 2013. We clarify medical data mining and its main goals. Therefore, each paper is studied based on the six medical tasks: screening, diagnosis, treatment, prognosis, monitoring and management. In each task, five data mining approaches are considered: classification, regression, clustering, association and hybrid. At the end of each task, a brief summarization and discussion are stated. A standard framework according to CRISP-DM is additionally adapted to manage all activities. As a discussion, current issue and future trend are mentioned. The amount of the works published in this scope is substantial and it is impossible to discuss all of them on a single work. We hope this paper will make it possible to explore previous works and identify interesting areas for future research. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： data mining application Medical data mining Medicine Disease data mining algorithms

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mining very large databases

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COMPUTER 1999年第8期32卷 38-+页

作者： Ganti, V Gehrke, J Ramakrishnan, R Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

The explosive growth of databases makes the scalability of data-mining techniques increasingly important. The authors describe algorithms that address three classical data mining problems.

关键词： classification clustering data analysis data analysis data mining data mining algorithms data mining techniques input data input database knowledge discovery in databases main memory market basket analysis pattern classification pattern clustering scalable algorithms strategic decision making vast data stores very large data sets very large database mining very large databases

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A statistical significance testing approach to mining the most informative set of patterns

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data mining AND KNOWLEDGE DISCOVERY 2014年第1期28卷 238-263页

作者： Lijffijt, Jefrey Papapetrou, Panagiotis Puolamaki, Kai Aalto Univ Dept Informat & Comp Sci Aalto 00076 Finland Univ London Dept Comp Sci & Informat Syst London WCIE 7HX England Finnish Inst Occupat Hlth FI-00025 Helsinki Finland

Hypothesis testing using constrained null models can be used to compute the significance of data mining results given what is already known about the data. We study the novel problem of finding the smallest set of patterns that explains most about the data in terms of a global p value. The resulting set of patterns, such as frequent patterns or clusterings, is the smallest set that statistically explains the data. We show that the newly formulated problem is, in its general form, NP-hard and there exists no efficient algorithm with finite approximation ratio. However, we show that in a special case a solution can be computed efficiently with a provable approximation ratio. We find that a greedy algorithm gives good results on real data and that, using our approach, we can formulate and solve many known data-mining tasks. We demonstrate our method on several data mining tasks. We conclude that our framework is able to identify in various settings a small set of patterns that statistically explains the data and to formulate data mining problems in the terms of statistical significance.

关键词： data mining algorithms Pattern mining Statistical significance testing

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A Method for mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2013年第4期25卷 721-733页

作者： Ji, Yanqing Ying, Hao Tran, John Dews, Peter Mansour, Ayman Massanari, R. Michael Gonzaga Univ Dept Elect & Comp Engn Spokane WA 99258 USA Wayne State Univ Dept Elect & Comp Engn Detroit MI 48202 USA Spokane Mental Hlth Spokane WA 99202 USA St Mary Mercy Hosp Trinity Hlth Dept Med Livonia MI 48154 USA Crit Junctures Inst Bellingham WA 98225 USA

In many real-world applications, it is important to mine causal relationships where an event or event pattern causes certain outcomes with low probability. Discovering this kind of causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient data sets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The algorithm was tested on real patient data retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The retrieved data included 16,206 patients (15,605 male, 601 female). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin, and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin, and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e. g., epidemiology study) and investigation (e. g., case review) by drug safety professionals.

关键词： Adverse drug reactions association rules data mining algorithms interestingness measure recognition primed decision model

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