KEYWORDS: Analytical research, Mining, Data mining, Feature selection, Information technology, Internet, Statistical analysis, Social sciences, Data communications, Social networks
With the development of Information Technology, people have entered the era of Big Data, and the demand for intelligent information is more intense. How to make computer provide more personalized and efficient service for all walks of life, is something worth exploring. In this paper, we aim to predict user’s character by analyzing the textual content of his/her micro-blog, which is the foundation of Personalized Service. Our study describes the method of creating a prediction model about user’s character by using Bayesian algorithms. Experimental results show that the Naïve Bayes approach is a valid and promoted analytic method in micro-blog character analysis.
KEYWORDS: Mining, Data conversion, Computer aided design, Data mining, Feature selection, Data modeling, Data hiding, Statistical modeling, Lawrencium, Information science
Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by using Chinese Word Segmentation, C5.0 and Apriori, and a set of experiments were run based on a collection of real textual data that includes 823 customer evaluations taken from a Chinese commercial bank. Results, consequent solutions, some advice for the commercial bank are given in this paper.
KEYWORDS: Image segmentation, 3D modeling, Analytical research, Neural networks, Information technology, Internet, Data mining, Factor analysis, Social sciences, Feature extraction
With fierce competition in banking industry, more and more banks have realised that accurate customer segmentation is of fundamental importance, especially for the identification of those high-value customers. In order to solve this problem, we collected real data about private banking customers of a commercial bank in China, conducted empirical analysis by applying K-means clustering technique. When determine the K value, we propose a mechanism that meet both academic requirements and practical needs. Through K-means clustering, we successfully segmented the customers into three categories, and features of each group have been illustrated in details.
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