The syndrome is the basic pathological unit and the key concept in traditional Chinese medicine (TCM), and the herbal remedy is prescribed according to the syndrome a patient catches. Nevertheless, few studies are dedicated to investigate the number of syndromes in chronic renal failure (CRF) patients and what these syndromes are. In this paper, we carry out a clinical epidemiology survey and obtain 601 CRF cases, including 72 symptoms in each report. Based on association delineated by mutual information, we propose a novel pattern discovery algorithm to discover syndromes, which probably have overlapped symptoms in TCM. A revised version of mutual information is presented here to discriminate positive and negative association. The algorithm self-organizedly discovers 16 effective patterns, each of which is verified manually by TCM physicians to recognize the syndrome it belongs to. The super-additivity of cluster by mutual information is proved and n-class association concept is introduced in our model to reduce computational complexity. Validation of the algorithm is performed by using the syndrome data and consolidated clinically to have 16 patterns. The results indicate that the algorithm achieves a high sensitivity with 96.48% and each classified pattern is of clinical significance. Therefore, we conclude that the algorithm provides an excellent solution to chronic renal failure problem in the context of traditional Chinese medicine.

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The theory of abstract neural automata has been proposed previously. Believes that in studying change of the structure of abstract neural automata, genetico-variable structure must be considered. Abstract neural automata are considered to be cognizant machines, machines of thought. The basic effect on cognition and on the thought of heredity, such as genes is considered to be important in this discussion. Following the proof of the theorem of existence of abstract neural automata whose state space of single neuron (stochastic automaton) is Euclidean space E d , the theorem of ergodicity of evolutionary process of genetico-variable structure of abstract neural automata is proved.