Data Mining Approach for Analyzing Call Center Performance
Marcin Paprzycki, Ajith Abraham, Ruiyuan Guo
发表信息：
The 17th International Conference on Industrial & Engineering Applications of Artificial Intelligence and Expert Systems, Canada, Springer Verlag, Germany, 2004 (forth coming)
The aim of our research was to apply well-known data mining techniques (such as linear neural networks, multi-layered perceptrons, probabilistic neural networks, classification and regression trees, support vector machines and finally a hybrid decision tree neural network approach) to the problem of predicting the quality of service in call centers; based on the performance data actually collected in a call center of a large insurance company. Our aim was two-fold. First, to compare the performance of models built using the above-mentioned techniques and, second, to analyze the characteristics of the input sensitivity in order to better understand the relationship between the perform-ance evaluation process and the actual performance and in this way help improve the performance of call centers. In this paper we summarize our findings.
A Framework for Analyzing Abnormal Emergence in Service Ecosystems Through LLM-based Agent Intention Mining
College of Intelligence and Computing（智能与计算学院）；Tianjin University（天津大学）；Tianjin Key Laboratory of Healthy Habitat and Smart Technology（天津健康人居与智能技术重点实验室）；Laboratory of Computation and Analytics of Complex Management Systems（复杂管理系统计算与分析实验室）；School of Software（软件学院）；Shandong University（山东大学）；Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR)（SDU-NTU人工智能研究联合中心（C-FAIR））；Faculty of Environment, Science and Economy（环境、科学与经济学院）
Shedding Light on Blind Spots: Developing a Reference Architecture to Leverage Video Data for Process Mining
FIM Research Center, University of Applied Sciences Augsburg（奥格斯堡应用技术大学FIM研究中心）；FIM Research Center, University of Bayreuth（拜罗伊特大学FIM研究中心）；Project Group Business & Information Systems Engineering of the Fraunhofer FIT（弗劳恩霍夫应用信息处理研究所商业与信息系统工程项目组）
On mining complex sequential data by means of FCA and pattern structures
LORIA (CNRS – Inria NGE – U. de Lorraine)（洛林大学、法国国家信息与自动化研究所、法国国家科学研究中心联合计算机研究实验室）；Orange Labs（奥兰治实验室）；National Research University Higher School of Economics（国立研究型高等经济学院）
