• Publications
  • Influence
Efficient Substructure Discovery from Large Semi-Structured Data
  • 446
  • 64
An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases
TLDR
In this paper, we propose an efficient algorithm LCM (Linear time Closed pattern Miner) for mining frequent closed patterns from large transaction databases. Expand
  • 258
  • 35
  • PDF
Linear-Time Longest-Common-Prefix Computation in Suffix Arrays and Its Applications
TLDR
We present a linear-time algorithm to simulate the bottom-up traversal of a suffix tree with a suffix array combined with the longest common prefix information in suffix arrays. Expand
  • 495
  • 32
  • PDF
LCM ver. 2: Efficient Mining Algorithms for Frequent/Closed/Maximal Itemsets
TLDR
We propose ecien t algorithms LCM (Linear time Closed itemset Miner), LCMfreq and LCMmax for these problems. Expand
  • 401
  • 28
  • PDF
LCM ver.3: collaboration of array, bitmap and prefix tree for frequent itemset mining
TLDR
We propose an efficient way to combine these three data structures so that in any case the combination gives the best performance. Expand
  • 205
  • 24
  • PDF
LCM: An Efficient Algorithm for Enumerating Frequent Closed Item Sets
TLDR
In this paper, we propose three algorithms LCMfreq, LCM, and LCMmax for mining all frequent set, frequent closed item sets, and maximal frequent sets, respectively, from transaction databases. Expand
  • 185
  • 16
  • PDF
Discovering Frequent Substructures in Large Unordered Trees
TLDR
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. Expand
  • 185
  • 14
  • PDF
Inductive inference of unbounded unions of pattern languages from positive data
TLDR
A pattern is a string consisting of constant symbols and variables. Expand
  • 51
  • 10
Optimized Substructure Discovery for Semi-structured Data
TLDR
In this paper, we consider the problem of discovering interesting substructures from a large collection of semi-structured data in the framework of optimized pattern discovery and present an efficient algorithm that discovers the best labeled ordered trees that optimize a given statistical measure, such as the information entropy and the classification accuracy. Expand
  • 166
  • 7
  • PDF
Learning Acyclic First-Order Horn Sentences from Entailment
  • H. Arimura
  • Mathematics, Computer Science
  • ALT
  • 6 October 1997
TLDR
We present an algorithm that exactly identifies every target Horn program H* in ACH(k) in polynomial time in p, m and n using O(pmnk+1) entailment equivalence and entailment membership queries. Expand
  • 57
  • 7