Efficient Algorithms to Detect and Restore Minimality, an Extension of the Regular Restriction of Resolution

A given binary resolution proof, represented as a binary tree, is said to be minimal if the resolutions cannot be reordered to generate an irregular proof. Minimality extends Tseitin"s regularity restriction and still retains completeness. A linear-time algorithm is introduced to decide whether a given proof is minimal. This algorithm can be used by a deduction system that avoids redundancy by retaining only minimal proofs and thus lessens its reliance on subsumption, a more general but more expensive technique.Any irregular binary resolution tree is made strictly smaller by an operation called Surgery, which runs in time linear in the size of the tree. After surgery the result proved by the new tree is nonstrictly more general than the original result and has fewer violations of the regular restriction. Furthermore, any nonminimal tree can be made irregular in linear time by an operation called Splay. Thus a combination of splaying and surgery efficiently reduces a nonminimal tree to a minimal one.Finally, a close correspondence between clause trees, recently introduced by the authors, and binary resolution trees is established. In that sense this work provides the first linear-time algorithms that detect minimality and perform surgery on clause trees.     

Inference of tree automata from sample set of trees

A method for inferring of tree automata from sample set of trees is presented. The procedure, which is based on the concept ofk-follower of a tree with respect to the sample tree set, produces a tree automaton capable of accepting all the sample trees as well as other trees similar in structure. The behavior of the inferred tree automaton for varying values of parameterk is also discussed.This work was supported in part by a Scientific Research Grant-In-Aid (Grant No. 57460129) from the Ministry of Education, Science and Culture, Japan.     

Maintaining Regular Properties Dynamically in k-Terminal Graphs

The t -topology tree data structure is developed for maintaining t -ary trees dynamically. Each of a certain set of tree operations is shown to take O(log n) time, where n is the number of vertices in the trees. The t -topology trees are used to maintain any given regular property on members of the family of k -terminal graphs under a variety of dynamic graph operations. Received February 1994; revised February 1995.     

A Flexible Algorithm for Generating All the Spanning Trees in Undirected Graphs

In this paper we propose an algorithm for generating all the spanning trees in undirected graphs. The algorithm requires O (n+m+ τ n) time where the given graph has n vertices, m edges, and τ spanning trees. For outputting all the spanning trees explicitly, this time complexity is optimal. Our algorithm follows a special rooted tree structure on the skeleton graph of the spanning tree polytope. The rule by which the rooted tree structure is traversed is irrelevant to the time complexity. In this sense, our algorithm is flexible. If we employ the depth-first search rule, we can save the memory requirement to O (n+m). A breadth-first implementation requires as much as O (m+ τ n) space, but when a parallel computer is available, this might have an advantage. When a given graph is weighted, the best-first search rule provides a ranking algorithm for the minimum spanning tree problem. The ranking algorithm requires O (n+ m + τ n) time and O (m+ τ n) space when we have a minimum spanning tree. Received January 21, 1995; revised February 19, 1996.     

Generation of t-ary trees with Ballot-sequences*

We define Ballot-sequences for t-ary trees based on the ideas of Ballot-sequences of binary trees presented by Rotem. Later an efficient algorithm for the generation of t-ary trees with Ballot-sequences in B-order is presented. The algorithm generates each sequence in constant average time O(1), and the sequences are generated in lexicographical order. The ranking and unranking algorithms with O(tn) time complexity are also described.     

Formalizing and Analyzing the Needham-Schroeder Symmetric-Key Protocol by Rewriting

This paper reports on work in progress on using rewriting techniques for the specification and the verification of communication protocols. As in Genet and Klay's approach to formalizing protocols, a rewrite system describes the steps of the protocol and an intruder's ability of decomposing and decrypting messages, and a tree automaton encodes the initial set of communication requests and an intruder's initial knowledge. In a previous work we have defined a rewriting strategy that, given a term t that represents a property of the protocol to be proved, suitably expands and reduces t using the rules in and the transitions in to derive whether or not t is recognized by an intruder. In this paper we present a formalization of the Needham-Schroeder symmetric-key protocol and use the rewriting strategy for deriving two well-known authentication attacks.     

A New Approach to Graph Recognition and Applications to Distance-Hereditary Graphs

Algorithms used in data mining and bioinformatics have to deal with huge amount of data efficiently.In many applications,the data are supposed to have explicit or implicit structures.To develop efficient algorithms for such data,we have to propose possible structure models and test if the models are feasible.Hence,it is important to make a compact model for structured data,and enumerate all instances efficiently.There are few graph classes besides trees that can be used for a model.In this paper,we inves...     

Creating Decision Trees from Rules using RBDT‐1

Most of the methods that generate decision trees for a specific problem use the examples of data instances in the decision tree–generation process. This article proposes a method called RBDT‐1—rule‐based decision tree—for learning a decision tree from a set of decision rules that cover the data instances rather than from the data instances themselves. The goal is to create on demand a short and accurate decision tree from a stable or dynamically changing set of rules. The rules could be generated by an expert, by an inductive rule learning program that induces decision rules from the examples of decision instances such as AQ‐type rule induction programs, or extracted from a tree generated by another method, such as the ID3 or C4.5. In terms of tree complexity (number of nodes and leaves in the decision tree), RBDT‐1 compares favorably with AQDT‐1 and AQDT‐2, which are methods that create decision trees from rules. RBDT‐1 also compares favorably with ID3 while it is as effective as C4.5 where both (ID3 and C4.5) are well‐known methods that generate decision trees from data examples. Experiments show that the classification accuracies of the decision trees produced by all methods under comparison are indistinguishable.     

Practical methods for constructing suffix trees

Sequence datasets are ubiquitous in modern life-science applications, and querying sequences is a common and critical operation in many of these applications. The suffix tree is a versatile data structure that can be used to evaluate a wide variety of queries on sequence datasets, including evaluating exact and approximate string matches, and finding repeat patterns. However, methods for constructing suffix trees are often very time-consuming, especially for suffix trees that are large and do not fit in the available main memory. Even when the suffix tree fits in memory, it turns out that the processor cache behavior of theoretically optimal suffix tree construction methods is poor, resulting in poor performance. Currently, there are a large number of algorithms for constructing suffix trees, but the practical tradeoffs in using these algorithms for different scenarios are not well characterized. In this paper, we explore suffix tree construction algorithms over a wide spectrum of data sources and sizes. First, we show that on modern processors, a cache-efficient algorithm with O(n²) worst-case complexity outperforms popular linear time algorithms like Ukkonen and McCreight, even for in-memory construction. For larger datasets, the disk I/O requirement quickly becomes the bottleneck in each algorithm's performance. To address this problem, we describe two approaches. First, we present a buffer management strategy for the O(n²) algorithm. The resulting new algorithm, which we call “Top Down Disk-based” (TDD), scales to sizes much larger than have been previously described in literature. This approach far outperforms the best known disk-based construction methods. Second, we present a new disk-based suffix tree construction algorithm that is based on a sort-merge paradigm, and show that for constructing very large suffix trees with very little resources, this algorithm is more efficient than TDD.     

Listing all the minimum spanning trees in an undirected graph

Efficient polynomial time algorithms are well known for the minimum spanning tree problem. However, given an undirected graph with integer edge weights, minimum spanning trees may not be unique. In this article, we present an algorithm that lists all the minimum spanning trees included in the graph. The computational complexity of the algorithm is O(N(mn+n ² log n)) in time and O(m) in space, where n, m and N stand for the number of nodes, edges and minimum spanning trees, respectively. Next, we explore some properties of cut-sets, and based on these we construct an improved algorithm, which runs in O(N m log n) time and O(m) space. These algorithms are implemented in C language, and some numerical experiments are conducted for planar as well as complete graphs with random edge weights.     

A linear-time algorithm to construct a rectilinear Steiner minimal tree fork-extremal point sets

Ak-extremal point set is a point set on the boundary of ak-sided rectilinear convex hull. Given ak-extremal point set of sizen, we present an algorithm that computes a rectilinear Steiner minimal tree in timeO(k ⁴ n). For constantk, this algorithm runs inO(n) time and is asymptotically optimal and, for arbitraryk, the algorithm is the fastest known for this problem.     

An Algorithm for Enumerating All Spanning Trees of a Directed Graph

We present an O(NV + V ³ ) time algorithm for enumerating all spanning trees of a directed graph. This improves the previous best known bound of O(NE + V+E) [1] when V ² =o(N) , which will be true for most graphs. Here, N refers to the number of spanning trees of a graph having V vertices and E edges. The algorithm is based on the technique of obtaining one spanning tree from another by a series of edge swaps. This result complements the result in the companion paper [3] which enumerates all spanning trees in an undirected graph in O(N+V+E) time. Received September 11, 1997; revised March 6, 1998.     

Improving on-line construction of two-dimensional suffix trees for square matrices

The two-dimensional (2-D) suffix tree of an n×n square matrix A is a compacted trie that represents all square submatrices of A. We consider constructing 2-D suffix trees on-line, which means, instead of giving the whole matrix A in advance, A is separated and each part of A is given at different time as algorithms proceed. In general, developing an on-line algorithm is more difficult than developing an off-line algorithm. Moreover, the smaller the input grain size is, the harder it is to develop an on-line algorithm. In the case of 2-D suffix tree construction, dealing with a character at a time is harder than dealing with a row or a column at a time.In this paper we propose a randomized linear-time algorithm for constructing 2-D suffix trees on-line. This algorithm is superior to previous algorithms in two ways: (1) This is the first linear-time algorithm for constructing 2-D suffix trees on-line. Although there have been some linear-time algorithms for off-line construction, there were no linear-time algorithms for on-line construction. (2) We deal with the most fine-grain on-line case, i.e., our algorithm can construct a 2-D suffix tree even though only one character of A is given at a time, while previous on-line algorithms require at least a row and/or a column at a time.     

Parallel breadth-first search algorithms for trees and graphs

Parallel Breadth-First Search (BFS) algorithms for ordered trees and graphs on a shared memory model of a Single Instruction-stream Multiple Data-stream computer are proposed. The parallel BFS algorithm for trees computes the BFS rank of eachnode of an ordered tree consisting of n nodes in time of 0(β log n) when 0(n ^1+1/β) processors are used, β being an integer greater than or equal to 2. The parallel BFS algorithm for graphs produces Breadth-First Spanning Trees (BFSTs) of a directedgraph G having n nodes in time 0(log d.log n) using 0(n ³) processors, where d is the diameter of G If G is a strongly connected graph or a connected undirected graph the BFS algorithm produces n BFSTs, each BFST having a different start node.     

On the finite degree of ambiguity of finite tree automata

Summary The degree of ambiguity of a finite tree automaton A, da(A), is the maximal number of different accepting computations of A for any possible input tree. We show: it can be decided in polynomial time whether or not da(A)<. We give two criteria characterizing an infinite degree of ambiguity and derive the following fundamental properties of an finite tree automaton A with n states and rank L>1 having a finite degree of ambiguity: for every input tree t there is a input tree t ₁ of depth less than 2²ⁿ·n! having the same number of accepting computations; the degree of ambiguity of A is bounded by 2^{2 2·log(L+1)·n}.     

Interactive learning of node selecting tree transducer

We develop new algorithms for learning monadic node selection queries in unranked trees from annotated examples, and apply them to visually interactive Web information extraction. We propose to represent monadic queries by bottom-up deterministic Node Selecting Tree Transducers (NSTTs), a particular class of tree automata that we introduce. We prove that deterministic NSTTs capture the class of queries definable in monadic second order logic (MSO) in trees, which Gottlob and Koch (2002) argue to have the right expressiveness for Web information extraction, and prove that monadic queries defined by NSTTs can be answered efficiently. We present a new polynomial time algorithm in RPNI-style that learns monadic queries defined by deterministic NSTTs from completely annotated examples, where all selected nodes are distinguished. In practice, users prefer to provide partial annotations. We propose to account for partial annotations by intelligent tree pruning heuristics. We introduce pruning NSTTs—a formalism that shares many advantages of NSTTs. This leads us to an interactive learning algorithm for monadic queries defined by pruning NSTTs, which satisfies a new formal active learning model in the style of Angluin (1987). We have implemented our interactive learning algorithm integrated it into a visually interactive Web information extraction system—called SQUIRREL—by plugging it into the Mozilla Web browser. Experiments on realistic Web documents confirm excellent quality with very few user interactions during wrapper induction. Editor: Georgios Paliouras and Yasubumi Sakakibara     

Decision tree approach for classification and dimensionality reduction of electronic nose data

This paper presents a decision tree approach using two different tree models, C4.5 and CART, for use in the classification and dimensionality reduction of electronic nose (EN) data. The decision tree is a tree structure consisting of internal and terminal nodes which process the data to ultimately yield a classification. The decision tree is proficient at both maintaining the role of dimensionality reduction and at organizing optimally sized classification trees, and therefore it could be a promising approach to analyze EN data. In the experiments conducted, six sensor response parameters were extracted from the dynamic sensor responses of each of the four metal oxide gas sensors. The six parameters observed were the rising time (T_r), falling time (T_f), total response time (T_t), normalized peak voltage change (y_p,n), normalized curve integral (C_I), and triangle area (T_A). One sensor parameter from each metal oxide sensor was used for the classification trees, and the best classification accuracy of 97.78% was achieved by CART using the C_I parameter. However, the accuracy of CART was improved using all of the sensor parameters as inputs to the classification tree. The improved results of CART, having an accuracy of 98.89%, was comparable to that of two popular classifiers, the multilayer perceptron (MLP) neural network and the fuzzy ARTMAP network (accuracy of 98.89%, and 100%, respectively). Furthermore, as a dimensionality reduction method the decision tree has shown a better discrimination accuracy of 100% for the MLP classifier and 98.89% for the fuzzy ARTMAP classifier as compared to those achieved with principle component analysis (PCA) giving 81.11% and 97.78%, and a variable selection method giving 92.22% and 93.33% (for the same MLP and fuzzy ARTMAP classifiers). Therefore, a decision tree could be a promising technique for a pattern recognition system for EN data in terms of two functions; as classifier which is an optimally organized classification tree, and as dimensionality reduction method for other pattern recognition techniques.     

On Maximal Frequent and Minimal Infrequent Sets in Binary Matrices

Given an m×n binary matrix A, a subset C of the columns is called t-frequent if there are at least t rows in A in which all entries belonging to C are non-zero. Let us denote by the number of maximal t-frequent sets of A, and let denote the number of those minimal column subsets of A which are not t-frequent (so called t-infrequent sets). We prove that the inequality (m–t+1) holds for any binary matrix A in which not all column subsets are t-frequent. This inequality is sharp, and allows for an incremental quasi-polynomial algorithm for generating all minimal t-infrequent sets. We also prove that the analogous generation problem for maximal t-frequent sets is NP-hard. Finally, we discuss the complexity of generating closed frequent sets and some other related problems.     

Canonical forms for labelled trees and their applications in frequent subtree mining

Tree structures are used extensively in domains such as computational biology, pattern recognition, XML databases, computer networks, and so on. In this paper, we first present two canonical forms for labelled rooted unordered trees–the breadth-first canonical form (BFCF) and the depth-first canonical form (DFCF). Then the canonical forms are applied to the frequent subtree mining problem. Based on the BFCF, we develop a vertical mining algorithm, RootedTreeMiner, to discover all frequently occurring subtrees in a database of labelled rooted unordered trees. The RootedTreeMiner algorithm uses an enumeration tree to enumerate all (frequent) labelled rooted unordered subtrees. Next, we extend the definition of the DFCF to labelled free trees and present an Apriori-like algorithm, FreeTreeMiner, to discover all frequently occurring subtrees in a database of labelled free trees. Finally, we study the performance and the scalability of our algorithms through extensive experiments based on both synthetic data and datasets from real applications.     

XML tree structure compression using RePair

XML tree structures can conveniently be represented using ordered unranked trees. Due to the repetitiveness of XML markup these trees can be compressed effectively using dictionary-based methods, such as minimal directed acyclic graphs (DAGs) or straight-line context-free (SLCF) tree grammars. While minimal SLCF tree grammars are in general smaller than minimal DAGs, they cannot be computed in polynomial time unless P=NP$\mathsf{P}=\mathsf{NP}$. Here, we present a new linear time algorithm for computing small SLCF tree grammars, called TreeRePair, and show that it greatly outperforms the best known previous algorithm BPLEX. TreeRePair is a generalization to trees of Larsson and Moffat's RePair string compression algorithm. SLCF tree grammars can be used as efficient memory representations of trees. Using TreeRePair, we are able to produce the smallest queryable memory representation of ordered trees that we are aware of. Our investigations over a large corpus of commonly used XML documents show that tree traversals over TreeRePair grammars are 14 times slower than over pointer structures and 5 times slower than over succinct trees, while memory consumption is only 1/43 and 1/6, respectively. With respect to file compression we are able to show that a Huffman-based coding of TreeRePair grammars gives compression ratios comparable to the best known XML file compressors.