אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Mining complex patterns from large data sets has attracted much attention in the last few decades. A plethora of methods and algorithms have been designed for mining a variety of patterns, ranging from simple association rules and frequent itemsets to advanced graph-based structures. However, as modern applications grow dramatically more sophisticated and operate on highly multidimensional and increasingly complex data, they introduce the demand for mining even more expressive and convoluted patterns unsupported by the current state-of-the-art techniques. As a result, more powerful and expressive pattern mining approaches are needed. We propose a novel method for multi-item multi-attribute pattern mining (MIMA-PM) - a generalization of classic pattern mining to substantially more expressive patterns. To the best of our knowledge, this work is the first to formally define and properly address this highly important problem. Extensive experimental evaluation conducted on synthetic and real-world data demonstrates high accuracy and scalability of our method.