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For many pattern recognition problems, the relation between the sample vectors and the class labels are known during the data acquisition procedure. However, how to find the useful rules or knowledge hidden in the data is very important and challengeable. Rule extraction methods are very useful in mining the important and heuristic knowledge hidden in the original high-dimensional data. It can help us to construct predictive models with few attributes of the data so as to provide valuable model interpretability and less training times.

In this paper, a novel rule extraction method with the application of biclustering algorithm is proposed.

To choose the most significant biclusters from the huge number of detected biclusters, a specially modified information entropy calculation method is also provided. It will be shown that all of the important knowledge is in practice hidden in these biclusters.

The novelty of the new method lies in the detected biclusters can be conveniently translated into if-then rules. It provides an intuitively explainable and comprehensive approach to extract rules from high-dimensional data while keeping high classification accuracy.

The review aims to facilitate a broader understanding of platform opening and cooperation and points out potential research directions for scholars.

This study searches Web of Science (WOS) database for relevant literature published between 2010 and 2021 and selects 86 papers for this review. The selected literature is categorized according to three dimensions: the strategic choice of platform opening and cooperation (before opening), the construction of an open platform (during opening) and the impact of platform opening and cooperation (after opening). Through comparative analysis, the authors identify research gaps and propose four future research agendas.

The study finds that the current studies are fragmented, and a research system with a theoretical foundation has not yet formed. In addition, with the development of platform operations, new topics such as platform ecosystems and open platform governance have emerged. In short, there is an urgent need for scholars to conduct exploratory research. To this end, the study proposes four future research agendas: strengthen basic research on platform opening and cooperation, deeply explore the dynamic evolution and cutting-edge models of platform opening and cooperation, analyze potential crises and impacts of platform openness and strengthen research on open platform governance.

This is the first systematic review on platform opening and cooperation. Through categorizing literature into three dimensions, this article clearly shows the research status and provides future research avenues.