Traditional clustering focuses on finding a single best clustering solution from data. However, given a single data set, one could interpret it in different ways. This is particularly true with complex data that has become prevalent in the data mining community: text, video, images and biological data to name a few. It is thus of practical interest to find all possible alternative and interesting clustering solutions from data. Recently there has been increasing interest on developing algorithms to discover multiple clustering solutions from complex data. This report provides a description of the first international workshop on this emerging topic --- SIGKDD MultiClust10: Discovering, Summarizing and Using Multiple Clusterings, which was held in Washington DC, on July 25th 2010. The workshop program consists of three invited talks and presentations of four full research papers and three short papers.