智之 内田

Bio: 智之 内田 is an academic researcher from Hiroshima City University. The author has contributed to research in topics: K-ary tree & Parallel algorithm. The author has an hindex of 1, co-authored 5 publications receiving 25 citations.

BONSAI Garden: Parallel Machine Discovery System for Sequences

Abstract: 1 Department of Physics, Kyushu University, Fukuoka 810, Japan 2 Department of Information Sciences, Hiroshima City University, Hiroshima 731-31, Japan 3 Research Institute of Fundamental Information Science, Kyushu University, Fukuoka 812, Japan 4 Department of Control Engineering and Science, Kyushu Institute of Technology, Iizuka 820, Japan 5 Department of Arti cial Intelligence, Kyushu Institute of Technology, Iizuka 820, Japan 6 Graduate School of Genetic Resources Technology, Kyushu University, Fukuoka 812, Japan

Stent delivery catheter

Abstract: PROBLEM TO BE SOLVED: To provide a stent delivery catheter with a novel structure, capable of efficiently placing a stent by an outer shaft with a simple structureSOLUTION: A distal end of a main shaft 22 is inserted into an outer distal shaft 20 and fixed thereto, a distal end of a core shaft 34 inserted into the main shaft is connected to an inner distal shaft 44 inserted into the outer distal shaft 20, and the inner distal shaft 44 is superposed with the main shaft 22 in a direction perpendicular to the axis In placement of a stent 28, a proximal end of the inner distal shaft 44 is moved relative to a distal position farther than the distal end of the main shaft 22 At least one of the proximal end face of the inner distal shaft 44 and the distal end face of the main shaft 22 includes a guide surface 54 that is inclined so as to protrude towards the axial direction with increasing distance from the superposed face of the inner distal shaft 44 and the main shaft 22SELECTED DRAWING: Figure 2

Discovery of Frequent Tree Structured Patterns in Semistructured Web Documents

Abstract: 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.

Ordered term tree languages which are polynomial time inductively inferable from positive data

Abstract: In the fields of data mining and knowledge discovery, many semistructured data such as HTML/XML files are represented by rooted trees t such that all children of each internal vertex of t are ordered and t has edge labels. In order to represent structural features common to such semistructured data, we propose a linear ordered term tree, which is a rooted tree pattern consisting of ordered tree structures and internal structured variables with distinct variable labels. For a set of edge labels Λ, let OTTΛ be the set of all linear ordered term trees. For a linear ordered term tree t in OTTΛ, the term tree language of t, denoted by LΛ (t), is the set of all ordered trees obtained from t by substituting arbitrary ordered trees for all variables in t. Given a set of ordered trees S, the minimal language problem for OTTLΛ = {LΛ (t) | t ∈ OTTΛ} is to find a linear ordered term tree t in OTTΛ such that LΛ (t) is minimal among all term tree languages which contain all ordered trees in S. We show that the class OTTLΛ is polynomial time inductively inferable from positive data, by giving a polynomial time algorithm for solving the minimal language problem for OTTLΛ.

Learning block-preserving graph patterns and its application to data mining

Abstract: Recently, due to the rapid growth of electronic data having graph structures such as HTML and XML texts and chemical compounds, many researchers have been interested in data mining and machine learning techniques for finding useful patterns from graph-structured data (graph data). Since graph data contain a huge number of substructures and it tends to be computationally expensive to decide whether or not such data have given structural features, graph mining problems face computational difficulties. Let ${\mathcal{C}}$ be a graph class which satisfies a connected hereditary property and contains infinitely many different biconnected graphs, and for which a special kind of the graph isomorphism problem can be computed in polynomial time. In this paper, we consider learning and mining problems for ${\mathcal{C}}$ . Firstly, we define a new graph pattern, which is called a block preserving graph pattern (bp-graph pattern) for ${\mathcal{C}}$ . Secondly, we present a polynomial time algorithm for deciding whether or not a given bp-graph pattern matches a given graph in ${\mathcal{C}}$ . Thirdly, by giving refinement operators over bp-graph patterns, we present a polynomial time algorithm for finding a minimally generalized bp-graph pattern for ${\mathcal{C}}$ . Outerplanar graphs are planar graphs which can be embedded in the plane in such a way that all of vertices lie on the outer boundary. Many pharmacologic chemical compounds are known to be represented by outerplanar graphs. The class of connected outerplanar graphs ${\mathcal{O}}$ satisfies the above conditions for ${\mathcal{C}}$ . Next, we propose two incremental polynomial time algorithms for enumerating all frequent bp-graph patterns with respect to a given finite set of graphs in ${\mathcal{O}}$ . Finally, by reporting experimental results obtained by applying the two graph mining algorithms to a subset of the NCI dataset, we evaluate the performance of the two graph mining algorithms.

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

Abstract: 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.

Discovering New Knowledge from Graph Data Using Inductive Logic Programming

Abstract: We present a method for discovering new knowledge from structural data which are represented by graphs in the framework of inductive logic programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. Formal Graph System (FGS) is a kind of logic programming system which directly deals with graphs just like first order terms. By employing refutably inductive inference algorithms and graph algorithmic techniques, we are developing a knowledge discovery system KD-FGS, which acquires knowledge directly from graph data by using FGS as a knowledge representation language. In this paper we develop a logical foundation of our knowledge discovery system. A term tree is a pattern which consists of variables and treelike structures. We give a polynomial-time algorithm for finding a unifier of a term tree and a tree in order to make consistency checks efficiently. Moreover we give experimental results on some graph theoretical notions with the system. The experiments show that the system is useful for finding new knowledge.