This paper proposes a spatio-temporal and categorical data mining method for commercial transaction data. This method searches for elements from a set using three search methods: specific, concept, and pattern, which represent the level of abstraction of search conditions, and spatio-temporal information and category information, which correspond to domain knowledge. This method aims to obtain knowledge of combinations of events with substantial physical, temporal, and categorical correlations between two data sets based on the amount of correlation and frequent occurrence patterns. In other words, it is to realize the mechanism in the database by which humans try to obtain knowledge through memory recall about the location, trends, timing, and frequency of events. This method uses aggregation functions to extract spatio-temporal and categorical features of elements in two different sets contained in a single set. Furthermore, this method performs Numerization, which converts the features from linguistic to numerical and Linguization, which converts the features from numerical and linguistic formats. The set elements are represented as vector data consisting of spatio-temporal and categorical features by numerical and linguistic formats. Numerical and linguistic formats are used for specific and conceptual searches, while linguistic formats are used for pattern searches. This method uses a dynamic vector creation function at search to dynamically map only those set elements that satisfy the search conditions into a semantic orthogonal space with time, space, and category dimensions. This method calculates correlation computation by calculating the distance between elements for each feature, normalizing and integrating their scores. Additionally, this method extracts as correlation rules combinations of events with substantial physical, temporal, and category correlations by calculating support and confidence levels based on the frequency of occurrence of the elements. Namely, Apriori algorithm is contained in the calculation for correlation rules. In this paper, we present the details of the proposed method and its implementation as an application in business commerce.