An Integrated Model for Financial Data Mining

Abstract

Nowadays, financial data analysis is becoming increasingly important in the business market. As companies collect more and more data from daily operations, they expect to extract useful knowledge from existing collected data to help make reasonable decisions for new customer requests, e.g. user credit category, churn analysis, real estate analysis, etc. Financial institutes have applied different data mining techniques to enhance their business performance. However, simple approach of these techniques could raise a performance issue. Besides, there are very few general models for both understanding and forecasting different financial fields. We present in this paper a new classification model for analyzing financial data. We also evaluate this model with different realworld data to show its performance.

Analysis of sales of vertical residential real estate projects in Goiânia and its influencing factors

“…One of the processes which help to solve this problem is Data Mining (DM). It consists of filtering and treating large databases, extracting data considered important and relevant which present value to the interested party, and in turn transforming what was filtered into information which was generally unknown (Cai et al, 2016). This often results in a significantly smaller database, but with very high information quality.…”