Mining Maximal Embedded Unordered Tree Patterns

Mining frequent tree patterns has many practical applications in areas such as XML document mining, Web mining, bioinformatics, network routing and so on. Most of the previous works used an apriori-based approach for candidate generation and frequency counting in their algorithms. In these approaches the state space grows exponentially since many unreal candidates are generated, especially when there are lots of large patterns among the data. To tackle these problems, we propose TDU, a top-down approach for mining all maximal, labeled, unordered, and embedded subtrees from a collection of tree-structured data. We would evaluate the effectiveness of the TDU algorithm in comparison to the previous works