MARC details
| 000 -LEADER |
|---|
| fixed length control field |
01899nam a2200289Ia 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210519s2019 xx 000 0 und d |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
|
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Byrnes, Paul E. |
| 245 #0 - TITLE STATEMENT |
|---|
| Title |
Automated clustering for data analytics / |
| Statement of responsibility, etc. |
Paul E. Byrnes |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Date of publication, distribution, etc. |
Fall 2019 |
| 336 ## - CONTENT TYPE |
|---|
| Content type term |
text |
| 337 ## - MEDIA TYPE |
|---|
| Media type term |
unmediated |
| 338 ## - CARRIER TYPE |
|---|
| Carrier type term |
volume |
| 440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Number of part/section of a work |
16 : 2, page 43-58 |
| Title |
Journal of Emerging Technologies in Accounting |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Today, auditors must consider the risks of material misstatement due to fraud during the financial statement audit (Messier, Glover, and Prawitt 2016). Current audit guidance recommends the use of data mining methods such as clustering to improve the likelihood of discovering irregularities during fraud risk assessment (ASB 2012). Unfortunately, significant challenges exist relative to using clustering in practice, including data preprocessing, model construction, model selection, and outlier detection. The traditional auditor is not trained to effectively address these complexities. One solution entails automation of clustering, thus eliminating the difficult, manual decision points within the clustering process. This would allow practitioners to focus on problem investigation and resolution, rather than being burdened with the technical aspects of clustering. In this paper, automated clustering is explored. In the process, each manual decision point is addressed, and a suitable automated solution is developed. Upon conclusion, a clustering application is formulated and demonstrated. |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
Accountancy. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Anomaly detection. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Auditing. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Clustering. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Data mining. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Exceptional exceptions. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Fraud discovery. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Irregularity detection. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Outlier detection. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Articles |
| 998 ## - LOCAL CONTROL INFORMATION (RLIN) |
|---|
| Cataloger's initials, CIN (RLIN) |
86181 |
| First Date, FD (RLIN) |
144544 |
