DryadeParent, An Efficient and Robust Closed Attribute Tree Mining Algorithm

Abstract

In this paper, we present a new tree mining algorithm, DRYADEPARENT, based on the hooking principle first introduced in DRYADE. In the experiments, we demonstrate that the branching factor and depth of the frequent patterns to find are key factors of complexity for tree mining algorithms, even if often overlooked in previous work. We show that DRYADEPARENT outperforms the current fastest algorithm, CMTreeMiner, by orders of magnitude on data sets where the frequent tree patterns have a high branching factor.Index Terms-Data mining, mining methods and algorithms, mining tree structured data.

Polynomial-Delay and Polynomial-Space Algorithms for Mining Closed Sequences, Graphs, and Pictures in Accessible Set Systems

“…In this paper, we mainly studied a group of pattern classes called rigid patterns [3,4,6,7,12,17,18,22], with closure mappings C. On the other hand, although classes of flexible patterns [5,28,27] have no notion of closure mappings C, they still allow efficient depth-first search. Thus, it an interesting future problem to extend the approach of this paper to these classes.…”

“…The problem of closed set mining (CIM) is one of the most extensively studied topics for years that attracts many researchers from both practical and theoretical views [3,4,5,6,7,11,12,17,18,22,25,26,28,27,31]. A closed itemset (a closed sets, for short) is a representative among an equivalence class of itemsets that have the same set of occurrences in common in a given transaction database D. The collection C of all closed sets in D contains the same information as the original collection F of frequent itemsets, while the size of C is possibly much smaller than F. Hence, from the engineering point of view, CIM is considered useful for increasing the comprehensibility and the efficiency of FIM [26,31].…”

“…On the other hand, some authors show that enumeration of closed patterns is non-trivial task than that of frequent patterns by proving the #P -hardness of the enumeration problem [7,30]. Furthermore, extensions of CIM for various subclasses of semi-structured data such as sequences, trees, and graphs are ongoing [3,4,5,6,7,12,17,18,22,28,27]. …”

Data Mining for XML Query-Answering Support

“…In the database literature, it is possible to find many proposals of algorithms to extract frequent structures both from graph-based data representations [36], [18], [15] and tree-based data representations [37], [35], [2], [25], [26], [17], [16], [23], [17]. In this paper, we focus on tree mining since XML documents are represented with a treeshaped structure.…”

Incremental Construction of Alpha Lattices and Association Rules

“…More recently, in order to deal with semi-structured data, more expressive patterns than itemsets have been heavily investigated [7,5]. Again closure operators have been used to efficiently extract closed patterns, as for instance attribute trees [1,11]. Still the number of closed patterns is often too high when dealing with realworld data, and some way to select them has to be found [6].…”