International Journal of Computer Science and Business Informatics IJCSBI.ORG ISSN: 1694-2108 | Vol. 14, No. 1. JUNE-JULY 2014 66 Present a Way to Find Frequent Tree Patterns using Inverted Index Saeid Tajedi Department of Computer Engineering Lorestan Science and Research Branch, Islamic Azad University Lorestan, Iran Hasan Naderi Department of Computer Engineering Iran University of Science and Technology Tehran, Iran ABSTRACT Among all patterns occurring in tree database, mining frequent tree is of great importance. The frequent tree is the one that occur frequently in the tree database. Frequent subtrees not only are important themselves but are applicable in other tasks, such as tree clustering, classification, bioinformatics, etc. In this paper, after reviewing different methods of searching for frequent subtrees, a new method based on inverted index is proposed to explore the frequent tree patterns. This procedure is done in two phases: passive and active. In the passive phase, we find subtrees on the dataset, and then they are converted to strings and will be stored in the inverted index. In the active phase easily, we derive the desired frequent subtrees by the inverted index. The proposed approach is trying to take advantage of times when the CPU is idle so that the CPU utilization is at its highest in in evaluation results. In the active phase, frequent subtrees mining is performed using inverted index rather than be done directly onto dataset, as a result, the desired frequent subtrees are found in the fastest possible time. One of the other features of the proposed method is that, unlike previous methods by adding a tree to the dataset is not necessary to repeat the previous steps again. In other words, this method has a high performance on dynamic trees. In addition, the proposed method is capable of interacting with the user. Keywords: Tree Mining, Inverted Index, Frequent pattern mining, tree patterns. 1. INTRODUCTION Data mining or knowledge discovery deals with finding interesting patterns or information that is hidden in large datasets. Recently, researchers have started proposing techniques for analyzing structured and semi-structured datasets. Such datasets can often be represented as graphs or trees. This has led to the development of numerous graph mining and tree mining algorithms in the literature. In this article we present an efficient algorithm for mining trees.