Online Induction of Probabilistic Real-Time Automata

作     者：Jana Schmidt Stefan Kramer 

作者机构：Technische Universitt MnchenInstitut fr Informatik/I12Boltzmannstr. 385748 Garching b. MnchenGermany Johannes Gutenberg-Universitt MainzInstitute for Computer ScienceStaudingerweg 955128 MainzGermany 

出 版 物：《Journal of Computer Science & Technology》 (计算机科学技术学报（英文版）)

年 卷 期：2014年第29卷第3期

页      面：345-360页

核心收录：

学科分类：081902[工学-矿物加工工程] 0819[工学-矿业工程] 08[工学] 081202[工学-计算机软件与理论] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：probabilistic real-time automata online induction maximum frequent pattern based clustering 

摘      要：The probabilistic real-time automaton （PRTA） is a representation of dynamic processes arising in the sciences and industry. Currently, the induction of automata is divided into two steps： the creation of the prefix tree acceptor （PTA） and the merge procedure based on clustering of the states. These two steps can be very time intensive when a PRTA is to be induced for massive or even unbounded datasets. The latter one can be efficiently processed, as there exist scalable online clustering algorithms. However, the creation of the PTA still can be very time consuming. To overcome this problem, we propose a genuine online PRTA induction approach that incorporates new instances by first collapsing them and then using a maximum frequent pattern based clustering. The approach is tested against a predefined synthetic automaton and real world datasets, for which the approach is scalable and stable. Moreover, we present a broad evaluation on a real world disease group dataset that shows the applicability of such a model to the analysis of medical processes.