Data Mining Techniques and Modelling for Financial Analysis

Gernel S. Lumacad, Sunil Mp
{"title":"Data Mining Techniques and Modelling for Financial Analysis","authors":"Gernel S. Lumacad, Sunil Mp","doi":"10.36647/ttidmkd/02.04.a002","DOIUrl":null,"url":null,"abstract":"Data mining methods are used to identify the needs of the customers and this also helps the companies to stay competitive in the global market. Effective data mining process allows the firms to stay in the running in the competitive world by looking into the strength of the company. Data mining process has the capability of analysing financial stability and that in turn helps the companies implicate suitable policy and strategy to achieve competitive advantages. There are several types of data mining process that helps the companies to analysis their needs and allows the companies to evaluate finances. Different the types have their own approaches and advantages and helps companies to assess their finances to make concrete decision. The study has been carried out with the help of suitable methods to secure the success of the study. Qualitative methods, inductive approach and cross-sectional research design has been utilised to accomplish the study. From the assessment it is evident that despite of the advantages there are some issues that has been faced by the companies is mainly due to the large size of the data and lack of expertise of the higher authority.","PeriodicalId":314032,"journal":{"name":"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36647/ttidmkd/02.04.a002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Data mining methods are used to identify the needs of the customers and this also helps the companies to stay competitive in the global market. Effective data mining process allows the firms to stay in the running in the competitive world by looking into the strength of the company. Data mining process has the capability of analysing financial stability and that in turn helps the companies implicate suitable policy and strategy to achieve competitive advantages. There are several types of data mining process that helps the companies to analysis their needs and allows the companies to evaluate finances. Different the types have their own approaches and advantages and helps companies to assess their finances to make concrete decision. The study has been carried out with the help of suitable methods to secure the success of the study. Qualitative methods, inductive approach and cross-sectional research design has been utilised to accomplish the study. From the assessment it is evident that despite of the advantages there are some issues that has been faced by the companies is mainly due to the large size of the data and lack of expertise of the higher authority.
财务分析的数据挖掘技术和建模
数据挖掘方法用于识别客户的需求,这也有助于公司在全球市场上保持竞争力。有效的数据挖掘过程可以让公司在竞争激烈的世界中保持领先地位,通过研究公司的实力。数据挖掘过程具有分析金融稳定性的能力,从而帮助公司制定合适的政策和战略,以获得竞争优势。有几种类型的数据挖掘过程可以帮助公司分析他们的需求,并允许公司评估财务。不同的类型有自己的方法和优势,帮助公司评估他们的财务做出具体的决策。本研究采用合适的方法进行,以确保研究的成功。本研究采用定性方法、归纳方法和横断面研究设计。从评估中可以明显看出,尽管有优势,但公司面临的一些问题主要是由于数据量大和缺乏上级权威的专业知识。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信