scholarly journals Audit Data Analysis and Application Based on Correlation Analysis Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jifan Chen ◽  
Muhammad Talha
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Correlation Analysis ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Accounting Data ◽  
Analysis Algorithm ◽  
Audit Data ◽  
Study Results ◽  
Depth Analysis

Traditional audit data analysis algorithms have many shortcomings, such as the lack of means to mine the hidden audit clues behind the data, the difficulty of finding increasingly hidden cheating techniques caused by the electronic and networked environment, and the inability to solve the quality defects of the audited data. Correlation analysis algorithm in data mining technology is an effective means to obtain knowledge from massive data, which can complete, muffle, clean, and reduce defective data and then can analyze massive data and obtain audit trails under the guidance of expert experience or analysts. Therefore, on the basis of summarizing and analyzing previous research works, this paper expounds the research status and significance of audit data analysis and application; elaborates the development background, current status, and future challenges of correlation analysis algorithm; introduces the methods and principles of data model and its conversion and audit model construction; conducts audit data collection and cleaning; implements audit data preprocessing and its algorithm description; performs audit data analysis based on correlation analysis algorithm; analyzes the hidden node activation value and audit rule extraction in correlation analysis algorithm; proposes the application of audit data based on correlation analysis algorithm; discusses the relationship between audit data quality and audit risk; and finally compares different data mining algorithms in audit data analysis. The findings demonstrate that by analyzing association rules, the correlation analysis algorithm can determine the significance of a huge quantity of audit data and characterise the degree to which linked events would occur concurrently or sequentially in a probabilistic manner. The correlation analysis algorithm first inputs the collected audit data through preprocessing module to filter out useless data and then organizes the obtained data into a format that can be recognized by data mining algorithm and executes the correlation analysis algorithm on the sorted data; finally, the obtained hidden data is divided into normal data and suspicious data by comparing it with the pattern in the rule base. The algorithm can conduct in-depth analysis and research on the company’s accounting vouchers, account books, and a large number of financial accounting data and other data of various natures in the company’s accounting vouchers; reveal its original characteristics and internal connections; and turn it into an audit. People need more direct and useful information. The study results of this paper provide a reference for further researches on audit data analysis and application based on correlation analysis algorithm.

Research on Data Mining Algorithm Based on Pattern Recognition

2019 ◽  
Vol 34 (06) ◽  
pp. 2059015 ◽  
Author(s):  
Xuelong Zhang
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Rapid Development ◽  
Future Trend ◽  
Support Vector ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Rapid Improvement ◽  
Training Time ◽  
Database Technology

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

Updating Audit Standard—Enabling Audit Data Analysis

2013 ◽  
Vol 27 (1) ◽  
pp. 325-331 ◽  
Author(s):  
William R. Titera
Keyword(s):  
Data Analysis ◽  
Audit Process ◽  
Private Company ◽  
Continuous Auditing ◽  
External Auditors ◽  
Financial Statement Audit ◽  
Audit Data ◽  
Depth Analysis ◽  
Audit Standard ◽  
Audit Standards

ABSTRACT This paper highlights the emerging role of data analysis on the financial statement audit and its value throughout the audit process, particularly in providing audit evidence. It raises the issue of needed revisions to the Audit Standards, whether for public or private company audits, and illustrates how certain of the current Audit Standards inhibit the external auditors' use of enhanced data analysis and continuous auditing techniques. While this whitepaper identifies a few audit standards that could be revised in light of current technological capabilities, it does not purport to address all needed revisions. Rather, it recommends that a more in-depth analysis be undertaken to develop needed guidance, as well as a list of recommended changes to the standards.

Data Mining in Conditions Analysis at a Coal-Fired Utility Boiler

2011 ◽  
Vol 403-408 ◽  
pp. 223-227
Author(s):  
Bao Ling Liu
Keyword(s):  
Data Mining ◽  
Information System ◽  
Power Plant ◽  
Working Conditions ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Utility Boiler ◽  
Mining Algorithm ◽  
The Way

The Supervisory Information System (SIS) [1]is widely installed in power plant of more than 300MW. Its massive data contains valuable information and resources which requires further excavation. In this paper a way of working conditions analysis based on cluster-based data mining algorithm is explored and experimented to SIS. The results illustrate that the way can identify and analyze the working conditions very well.

Secure Data Analysis in Clusters (Iris Database)

2017 ◽  
pp. 52-61
Author(s):  
Raghvendra Kumar ◽  
Prasant Kumar Pattnaik ◽  
Priyanka Pandey
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Privacy Preservation ◽  
Data Communication ◽  
Distributed Database ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Data Set ◽  
Mining Algorithm ◽  
Secure Data

This chapter used privacy preservation techniques (Data Modification) to ensure Privacy. Privacy preservation is another important issue. A picture, where number of clients owning their clustered databases (Iris Database) wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information and requires the privacy of the privileged information. There are numbers of efficient protocols are required for privacy preserving in data mining. This chapter presented various privacy preserving protocols that are used for security in clustered databases. The Xln(X) protocol and the secure sum protocol are used in mutual computing, which can defend privacy efficiently. Its focuses on the data modification techniques, where it has been modified our distributed database and after that sanded that modified data set to the client admin for secure data communication with zero percentage of data leakage and also reduce the communication and computation complexity.

Data mining algorithm for correlation analysis of industrial alarms

2017 ◽  
Vol 22 (S4) ◽  
pp. 10133-10143 ◽  
Author(s):  
Tingshun Li ◽  
Wen Tan ◽  
Xiaojie Li
Keyword(s):  
Data Mining ◽  
Correlation Analysis ◽  
Data Mining Algorithm ◽  
Mining Algorithm

Implementation of Hierarchical Database Attributes Covering in Multidetector A-R Mining Algorithm

2012 ◽  
Vol 151 ◽  
pp. 560-564
Author(s):  
Shu Feng Jiang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Data Mining Algorithm ◽  
Application Development ◽  
Association Rules Mining ◽  
Algorithm Efficiency ◽  
Attribute Weights ◽  
Mining Algorithm ◽  
Depth Analysis ◽  
Classical Association

With the development of artificial intelligence and data warehouse application development,Intelligent and efficient data mining technology has become the huge data bottleneck, This paper studies stratification theory improved technology to realize the property overrides hierarchical database, Data pretreatment based on the mining algorithm, Through in-depth analysis and research, Improved the A-R algorithm, Realize the problem scope expanded and improved the classical association rules mining algorithm efficiency, Based on the realization of the multilevel association rules mining based on the attribute weights of attributes covering hierarchical database mining methods,To improve the mining knowledge representation systems automation capabilities and data mining algorithm and its application to extended practical problems

scholarly journals An In-Depth Analysis of The Data Mining Algorithms and Their Effective Applicability in Health and Biomedical Informatics

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Deeya Tangri
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Medical Science ◽  
Massive Data ◽  
Medical Field ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
Depth Analysis ◽  
Data Extract ◽  
Mining Algorithms

Nowadays, the Health care industry is one of the fastest-growing industries. As we already know, health care has researched very widely, introducing many medical data that is not easy to mine. Data mining is an approach that helps to discover essential data from massive data or collection of data. So, in medical Science, there is a need for tools that help analyses the data, extract the significant result from massive data, and discover efficient use of information. Generally, three things are mandatory in medical for every patient. First is patient details, diagnosis and medications. Converting these data into a basic pattern for predicting the patient disease helps in early diagnosis. This research mainly focuses on the data mining approach, which is widely considered in the medical field.

Massive Data Mining Algorithm for Web Text Based on Clustering Algorithm

2019 ◽  
Vol 23 (2) ◽  
pp. 362-365 ◽  
Author(s):  
Nan-Chao Luo ◽  
Keyword(s):  
Data Mining ◽  
Vector Space ◽  
Clustering Algorithm ◽  
Current Data ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Quality Of Data ◽  
Chi Square ◽  
Data Mining Algorithms ◽  
Mining Algorithm

The massive data of Web text has the characteristics of high dimension and sparse spatial distribution, which makes the problems of low mining precision and long time consuming in the process of mining mass data of Web text by using the current data mining algorithms. To solve these problems, a massive data mining algorithm of Web text based on clustering algorithm is proposed. By using chi square test, the feature words of massive data are extracted and the set of characteristic words is gotten. Hierarchical clustering of feature sets is made, TF-IDF values of each word in clustering set are calculated, and vector space model is constructed. By introducing fair operation and clone operation on bee colony algorithm, the diversity of vector space models can be improved. For the result of the clustering center, K-means is introduced to extract the local centroid and improve the quality of data mining. Experimental results show that the proposed algorithm can effectively improve data mining accuracy and time consuming.

Prototype Analysis for Business Intelligence Utilization in Data Mining Analysis

2017 ◽  
Vol 7 (7) ◽  
pp. 30
Author(s):  
M. Venkata Krishna Rao ◽  
Ch Suresh ◽  
K. Kamakshaiah ◽  
M. Ravikanth
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Business Intelligence ◽  
System Analysis ◽  
Resource Planning ◽  
Business Community ◽  
Data Systems ◽  
Report Generation ◽  
Depth Analysis ◽  
Transactional Data

Tremendous increase of high availability of more disparate data sources than ever before have raised difficulties in simplifying frequent utility report across multiple transaction systems apart from an integration of large historical data. It is main focusing concept in data exploration with high transactional data systems in real time data processing.  This problem mainly occurs in data warehouses and other data storage proceedings in Business Intelligence (BI) for knowledge management and business resource planning. In this phenomenon, BI consists software construction of data warehouse query processing in report generation of high utility data mining in transactional data systems. The growth of a huge voluminous data in the real world is posing challenges to the research and business community for effective data analysis and predictions. In this paper, we analyze different data mining techniques and methods for Business Intelligence in data analysis of transactional databases. For that, we discuss what the key issues are by performing in-depth analysis of business data which includes database applications in transaction data source system analysis. We also discuss different integrated techniques in data analysis in business operational process for feasible solutions in business intelligence

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