In data integration applications, a join matches elements thatare common to two data sources. Often, however, elements are represented slightly different in each source, so an approximate join must be used. For XML data, most approximate join strategies are based on some ordered tree matching technique. But in data-centric XML the order is irrelevant: two elements should match even if their subelement order varies. In this paper we give a solution for the approximate join of unordered trees. Our solution is based on windowed pq-grams. We develop an efficient technique to systematically generate windowed pq-grams in a three-step process: sorting the unordered tree, extending the sorted tree with dummy nodes, and computing the windowed pq-grams on the extended tree. The windowed pq-gram distance between two sorted trees approximates the tree edit distance between the respective unordered trees. The approximate join algorithm based on windowed pq-grams is implemented as an equality join on strings which avoids the costly computation of the distance between every pair of input trees. Our experiments with synthetic and real world data confirm the analytic results and suggest that our technique is both useful and scalable.