Self-Directed Learning, A Guide for Learners and Teachers

Dimensions of Reinforcement Learning

Efficient substructure discovery from large semi-structured data

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Efficient Substructure Discovery from Large Semi-structed Data

In this paper, we study a frequent substructure discovery problem in semi-structured data. We present an efficient algorithm Unotthat computes all frequent labeled unordered trees appearing in a large collection of data trees with frequency above a user-specified threshold. The keys of the algorithm are efficient enumeration of all unordered trees in canonical form and incremental computation of their occurrences. We then show that Unotdiscovers each frequent pattern T in O(kb 2 m) per pattern, where k is the size of T, b is the branching factor of the data trees, and m is the total number of occurrences of T in the data trees.

Discovering Frequent Substructures in Large Unordered Trees

Many documents such as Web documents or XML files have no rigid structure. Such semistructured documents have been rapidly increasing. We propose a new method for discovering frequent tree structured patterns in semistructured Web documents. We consider the data mining problem of finding all maximally frequent tag tree patterns in semistructured data such as Web documents. A tag tree pattern is an edge labeled tree which has hyperedges as variables. An edge label is a tag or a keyword inWeb documents, and a variable can be substituted by any tree. So a tag tree pattern is suited for representing tree structured patterns in semistructured Web documents. We present an algorithm for finding all maximally frequent tag tree patterns. Also we report some experimental results on XML documents by using our algorithm.

Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents

Many Web documents such as HTML files and XML files have no rigid structure and are called semistructured data. In general, such semistructuredWeb documents are represented by rooted trees with ordered children. We propose a new method for discovering frequent tree structured patterns in semistructured Web documents by using a tag tree pattern as a hypothesis. A tag tree pattern is an edge labeled tree with ordered children which has structured variables. An edge label is a tag or a keyword in such Web documents, and a variable can be substituted by an arbitrary tree. So a tag tree pattern is suited for representing tree structured patterns in such Web documents. First we show that it is hard to compute the optimum frequent tag tree pattern. So we present an algorithm for generating all maximally frequent tag tree patterns and give the correctness of it. Finally, we report some experimental results on our algorithm. Although this algorithm is not efficient, experiments show that we can extract characteristic tree structured patterns in those data.

Discovery of Frequent Tag Tree Patterns in Semistructured Web Documents

We present a two-phase genetic algorithm (TGA) to solve timetabling problems for universities. Here, we use two kinds of populations. The first population is related to class scheduling, and the second one is related to room allocation. These populations are evolved independently, and a cost value of each individual is calculated. Then several individuals with the lowest costs are selected from each population, and these individuals are combined in order to calculate the fitness values. To evaluate the performance of TGA, we apply TGA to several timetabling problems and compare results obtained by TGA with those obtained by the simple GA (SGA). For the timetabling problem based on the curriculum of the Faculty of Information Sciences at Hiroshima City University, TGA finds a solution that satisfies all constraints, but SGA cannot find a feasible solution. From the results for problems generated by an automatic timetabling problem generator, we show that TGA obtains a better solution than the simple GA when the room utilization ratio is high.

A Co-evolving Timeslot/Room Assignment Genetic Algorithm Technique for University Timetabling

Graphs have enough richness and flexibility to express discrete structures hidden in a large amount of data. Some searching methods utilizing graph algorithmic techniques have been developed in Knowledge Discovery. A term graph, which is one of expressions for graph-structured data, is a hypergraph whose hyperedges are regarded as variables. Although term graphs can represent complicated patterns found from structured data, it is hard to do pattern match and pattern search in them. We have been studying subclasses of term graphs, called regular term trees, which are suited for expressing tree-like structured data. In this paper, we consider a matching problem for a regular term tree t and a standard tree T, which decides whether or not there exists a tree T′ such that T′ is isomorphic to T and T′ is obtained by replacing variables in t with some trees. First we show that the matching problem for a regular term tree and a tree is NP-complete even if each variable in the regular term tree contains only 4 vertices. Next we give a polynomial time algorithm for solving the matching problem for a regular term tree and a tree of bounded degree such that the regular term tree has only variables consisting the constant number of vertices greater than one. We also report some computational experiments and compare our algorithm with a naive algorithm.

Polynomial Time Matching Algorithms for Tree-Like Structured Patterns in Knowledge Discovery

We propose a genetic programming approach to extraction of glycan motifs by using tag tree patterns, which are tree structured patterns with structured variables. A structured variable in a tag tree pattern can be substituted by an arbitrary tree. Our experiments show that we have obtained tag tree patterns as the best individuals including similar substructures of glycan motifs obtained by the previous works.

A genetic programming approach to extraction of glycan motifs using tree structured patterns

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