Abstract
The analysis and representation of temporal data ar e becoming increasingly important in many areas of research and application. The existing Fuzzy Cognit ive Maps (FCMs) are efficient modeling method for knowledge representation and fuzzy reasoning in time series analysis. In the past, it was used to repr esent a complex causal system as a collection of concepts a nd causal relationships among concepts. However, most of the FCMs available now are constructed manually and are constrained with human experts’ interventio n for assessing its reliability. This study proposes a new temporal mining system to discover temporal dependencies between the concepts of a complex causal system by building a Fuzzy Temporal Cognitive Map (FTCM) by extending the FCM. For this purpose, a four-layer fuzzy temporal neural network is proposed and implemented by the automatic creation of the conventional FTCMs from the given data. This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cogn etive maps into a fuzzy temporal rule based inferen ce system using Allen’s temporal relationships and fuz zy temporal rules.
Highlights
Temporal data mining is the process of extracting temporal patterns from large collection of data
This study proposes a new temporal mining system to discover temporal dependencies between the concepts of a complex causal system by building a Fuzzy Temporal Cognitive Map (FTCM) by extending the Fuzzy Cognitive Maps (FCMs)
A four-layer fuzzy temporal neural network is proposed and implemented by the automatic creation of the conventional FTCMs from the given data. This FTCM is generated from the medical temporal database records of diabetic patients where the medical diagnosis is performed by converting the fuzzy cognetive maps into a fuzzy temporal rule based inference system using Allen’s temporal relationships and fuzzy temporal rules
Summary
Temporal data mining is the process of extracting temporal patterns from large collection of data. It applies methods such as clustering, neural networks, genetic algorithms, decision trees, to mine data with the intention of uncovering hidden temporal patterns. The prediction of future values in a time series system is based upon past/ present information and it is very useful in medical applications. Time series analysis is more applicable in temporal clinical databases to predict a patient’s health and to plan medical therapy. Medical data is temporal in nature and conventional data mining techniques are not suitable to make effective decisions in medical applications. Medical ontologies fail to map the symbolic knowledge with numerical knowledge in order to perform inference. Fuzzy Cognitive Map (FCM) has several properties such as flexibility, abstraction, (Stylios and Groumpos, 2004) differentiability as well as fuzzy reasoning and is capable of mapping the symbolic knowledge to numerical knowledge
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