purpose: this paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
design/methodology/approach: a variety of methods such as the model construction, system analysis and experiments are used. the author has improved morris' crossmapping technique and developed a technique for directly describing, visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
findings: the visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. it can reveal more and in-depth information than analyzing co-occurrence relations between two entities. therefore, this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.
research limitations: the technique could only be used to analyze co-occurrence relations of less than three entities at present.
practical implications: this research has expanded the study scope of co-occurrence analysis. the research result has provided a theoretical support for co-occurrence analysis.
originality/value: there has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. this research defines multiple co-occurrence and the research scope, develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.

purpose: this paper explores a method of knowledge discovery by visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
design/methodology/approach: a variety of methods such as the model construction, system analysis and experiments are used. the author has improved morris' crossmapping technique and developed a technique for directly describing, visualizing and analyzing co-occurrence relations among three or more entities in collections of journal articles.
findings: the visualization tools and the knowledge discovery method can efficiently reveal the multiple co-occurrence relations among three entities in collections of journal papers. it can reveal more and in-depth information than analyzing co-occurrence relations between two entities. therefore, this method can be used for mapping knowledge domain that is manifested in association with the entities from multi-dimensional perspectives and in an all-round way.
research limitations: the technique could only be used to analyze co-occurrence relations of less than three entities at present.
practical implications: this research has expanded the study scope of co-occurrence analysis. the research result has provided a theoretical support for co-occurrence analysis.
originality/value: there has not been a systematic study on co-occurrence relations among multiple entities in collections of journal articles. this research defines multiple co-occurrence and the research scope, develops the visualization analysis tool and designs the analysis model of the knowledge discovery method.