Abstract
In order to model multi-dimensions and multi-granularities oriented complex systems, this paper firstly proposes a kind of multi-relational Fuzzy Cognitive Map (FCM) to simulate the multi-relational system and its auto construct algorithm integrating Nonlinear Hebbian Learning (NHL) and Real Code Genetic Algorithm (RCGA). The multi-relational FCM fits to model the complex system with multi-dimensions and multi-granularities. The auto construct algorithm can learn the multi-relational FCM from multi-relational data resources to eliminate human intervention. The Multi-Relational Data Mining (MRDM) algorithm integrates multi-instance oriented NHL and RCGA of FCM. NHL is extended to mine the causal relationships between coarse-granularity concept and its fined-granularity concepts driven by multi-instances in the multi-relational system. RCGA is used to establish high-quality high-level FCM driven by data. The multi-relational FCM and the integrating algorithm have been applied in complex system of Mutagenesis. The experiment demonstrates not only that they get better classification accuracy, but it also shows the causal relationships among the concepts of the system.
Highlights
The aim of the paper is to auto simulate complex systems with multi-dimensions and multi-granularities driven by multi-relational data resources for better classification and causal relationships of a system.Multi-Relational Data Mining (MRDM) [1,2] is able to discover knowledge directly from multi-relational data tables, not through connection and aggregation of multiple relational data into a single data
We construct a kind of multi-levels and multi-dimensions Fuzzy Cognitive Map (FCM) to automatic model complex systems directly from multi-relational data resources
In the FCM, one concept in high-level has a summary evaluation in a dimension, which is inferred by the transformation function of low-level FCM
Summary
The aim of the paper is to auto simulate complex systems with multi-dimensions and multi-granularities driven by multi-relational data resources for better classification and causal relationships of a system.Multi-Relational Data Mining (MRDM) [1,2] is able to discover knowledge directly from multi-relational data tables, not through connection and aggregation of multiple relational data into a single data. Multi-relationship data mining can effectively prevent the problems of information loss, statistical deviation and low efficiency, etc. These methods [3,4,5] such as CrossMine, MI-MRNBC and Graph-NB are fitting for multi-relational data mining, but cannot obtain causality in a multi-relational system. FCM, as a kind of graph model, combines some aspects from fuzzy logic, neural networks and other techniques, and is fitting for modeling system from data resources. Each compound includes all attributes that are logp and lumo of mole besides those in BK1. For better operation efficiency in shorter runtime, an experiment, based on multi-relationship FCM and the integrated algorithm, has been carried out in the different fitness thresholds under three kinds of background. When the fitness threshold is at the interval of
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