Feature Selection, Extraction and Construction (2008)
Feature selection is a process that chooses a subset of features from the original features so that the feature space is optimally reduced according to a certain criterion. Feature...
Takashi Washio, Fuminori Adachi, Hiroshi Motoda
This paper proposes a novel approach to discover simultaneous time differential law equations having high plausibility to represent first principles underlying objective processes. The approach has...
Analysis of Hepatitis Dataset by Decision Tree Graph-Based Induction ⋆ (2008)
Kouzou Ohara, Tetsuya Yoshida, Warodom Geamsakul, Hiroshi Motoda, Takashi Washio, Hideto Yokoi, ...
Abstract. We analyzed the hepatitis data by Decision Tree Graph-Based Induction (DT-GBI), which constructs a decision tree for graphstructured data while simultaneously constructing attributes for...
Mining Discriminative Patterns from Graph Structured Data with Constrained Search (2008)
Kiyoto Takabayashi, Phu Chien Nguyen, Kouzou Ohara, Hiroshi Motoda, Takashi Washio
Abstract. A graph mining method, Chunkingless Graph-Based Induction (Cl-GBI), finds typical patterns that appear in graph structured data by the operation called chunkingless pairwise expansion which...
A History-oriented Envisioning Method (2008)
Takashi Washio, Hiroshi Motoda
A novel and generic approach named as "history-oriented envisioning " is proposed to qualitatively envision all the possible and the sound situations focusing on our intended...
A Framework of Numerical Basket Analysis (2008)
Takashi Washio, Atsushi Fujimoto, Hiroshi Motoda
Basket Analysis is mathematically characterized and extended to search families of sets in this paper. These theories indicate the possibility of various new approaches of data mining. We demonstrate...
Using a Hash-based Method for Apriori-based Graph Mining (2008)
Phu Chien Nguyen, Takashi Washio, Kouzou Ohara, Hiroshi Motoda
Abstract. The problem of discovering frequent subgraphs of graph data can be solved by constructing a candidate set of subgraphs first, and then, identifying within this candidate set those subgraphs...
Development of Generic Search Method Based on Transformation Invariance (2008)
Fuminori Adachi, Takashi Washio, Atsushi Fujimoto, Hiroshi Motoda, Hidemitsu Hanafusa
Abstract. The needs of efficient and flexible information retrieval on multistructural data stored in database and network are significantly growing. Especially, its flexibility plays one of key...
Development of Generic Search Method Based on Transformation Invariance (2008)
Fuminori Adachi, Takashi Washio, Hiroshi Motoda, Hidemitsu Hanafusa
The needs of efficient and flexible information retrieval on multi-structural data stored in database and network are significantly growing. Especially, its flexibility plays one of key roles to...
IOS Press A General Framework for Mining Frequent Subgraphs from Labeled Graphs (2008)
Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
Abstract. The derivation of frequent subgraphs from a dataset of labeled graphs has high computational complexity because the hard problems of isomorphism and subgraph isomorphism have to be solved...
Nada Lavrac, Hiroshi Motoda, Tom Fawcett
Abstract. The architecture of Blue Martini Software’s e-commerce suite has supported data collection, transformation, and data mining since its inception. With clickstreams being collected at the...
Characterization of Default Knowledge in (2008)
Takuya Wada, Tadashi Horiuchi, Hiroshi Motoda, Takashi Washio, Takuya Wada, Tadashi Horiuchi, ...
Abstract. \Ripple Down Rules (RDR) " Method is one of the promising approaches to directly acquire and encode knowledge from human experts. It requires data to be supplied incrementally to...
Compact Dual Ensembles for Active Learning (2008)
Amit M, Huan Liu, Hiroshi Motoda
Abstract. Generic ensemble methods can achieve excellent learning performance, but are not good candidates for active learning because of their different design purposes. We investigate how to use...
Machine Learning, 57, 145--175, 2004 c (2008)
Learning To Decode, Rebecca Hutchinson, Francisco Pereira, Xuerui Wang, Marcel Just, Sharlene Newman, ...
Over the past decade, functional Magnetic Resonance Imaging (fMRI) has emerged as a powerful new instrument to collect vast quantities of data about activity in the human brain. A typical fMRI...
Top 10 algorithms in data mining (2008)
Wu, Xindong, Kumar, Vipin, Quinlan, J. Ross, Ghosh, Joydeep, Yang, Qiang, Motoda, Hiroshi, ...
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Top 10 algorithms in data mining (2008)
Wu, Xindong, Kumar, Vipin, Quinlan, J. Ross, Ghosh, Joydeep, Yang, Qiang, Motoda, Hiroshi, ...
Top 10 algorithms in data mining (2008)
Wu, Xindong, Kumar, Vipin, Quinlan, J. Ross, Ghosh, Joydeep, Yang, Qiang, Motoda, Hiroshi, ...
Acquiring (Ir)relevance Knowledge for Problem Solving (2007)
Alon Levy, Hiroshi Motoda, Yumi Iwasaki
A major drawback of artificial intelligence systems that rely on declarative representations is that the efficiency of reasoning degrades quickly as the size of the knowledge base increases. To...
Consistency Based Feature Selection (2007)
Manoranjan Dash, Huan Liu, Hiroshi Motoda
. Feature selection is an effective technique in dealing with dimensionality reduction for classification task, a main component of data mining. It searches for an "optimal" subset of...
Automated Scientific Modeling from Observed Data and its Application to Socio-Psychology (2007)
Takashi Washio, Hiroshi Motoda, Yuji Niwa
The knowledge-based automated modeling framework such as CML can be applied only to the systems where their valid background knowledge is available. The conventional model equation discovery systems...
Applying Algebraic Mining Method of Graph Substructures to Mutageniesis Data Analysis (2007)
Akihiro Inokuchi, Takashi Washio, Takashi Okada, Hiroshi Motoda
this paper, one graph constitutes one transaction. The graph structured data can be transformed without much computational effort into an adjacency matrix whichisavery well known representation of a...
Extracting Behavioral Patterns from Relational History Data (2007)
Hiroshi Motoda, Takashi Washio, Toshihiro Kayama, Kenichi Yoshida
Identifying user-dependent information that can be automatically collected helps build a user model by which to predict what the user wants to do next. Such information is often relational and is not...
DOI 10.1007/s10115-007-0114-2 SURVEY PAPER Top 10 algorithms in data mining (2007)
Xindong Wu, Vipin Kumar, J. Ross, Quinlan Joydeep, Ghosh Qiang Yang, Hiroshi Motoda, ...
Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank,...
Editorial: Data Mining Lessons Learned (2004)
Nada Lavrac, Hiroshi Motoda, Tom Fawcett
Introduction Data mining is concerned with finding interesting patterns in data. Many techniques have emerged for analyzing and visualizing large volumes of data. What one finds in the technical...
A selective sampling approach to active feature selection (2004)
Huan Liu, Hiroshi Motoda, Lei Yu
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result...
Consumer behavior analysis by graph mining technique (2004)
Katsutoshi Yada, Hiroshi Motoda, Takashi Washio, Asuka Miyawaki
Abstract. In this paper we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for...
A selective sampling approach to active feature selection (2004)
Huan Liu, Hiroshi Motoda, Lei Yu
Feature selection, as a preprocessing step to machine learning, has been very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result...
A Bias-Variance Analysis of a Real World Learning Problem: The CoIL Challenge 2000 (2004)
Nada Lavrač, Hiroshi Motoda, Tom Fawcett
Abstract. The CoIL Challenge 2000 data mining competition attracted a wide variety of solutions, both in terms of approaches and performance. The goal of the competition was to predict who would be...
Active feature selection using classes (2003)
Huan Liu, Lei Yu, Manoranjan Dash, Hiroshi Motoda
Abstract. Feature selection is frequently used in data pre-processing for data mining. When the training data set is too large, sampling is commonly used to overcome the difficulty. This work...
T.: Constructing a decision tree for graph structured data (2003)
Warodom Geamsakul, Takashi Matsuda, Tetsuya Yoshida, Hiroshi Motoda, Takashi Washio
Abstract. Decision tree Graph-Based Induction (DT-GBI) is proposed that constructs a decision tree for graph structured data. Substructures (patterns) are extracted at each node of a decision tree by...
Complete mining of frequent patterns from graphs: Mining graph data (2003)
Abstract. Basket Analysis, which is a standard method for data mining, derives frequent itemsets from database. However, its mining ability is limited to transaction data consisting of items. In...
Mining patterns from structured data by beam-wise graph-based induction (2002)
Takashi Matsuda, Hiroshi Motoda, Tetsuya Yoshida, Takashi Washio
Abstract. Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). It is very efficient because of its greedy search strategy but at the...
Feature Selection with Selective Sampling (2002)
Huan Liu, Hiroshi Motoda, Lei Yu
Feature selection, as a preprocessing step to machine learning, has been shown very effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving...
On Issues of Instance Selection (2002)
The digital technologies and computer advances with the booming internet uses have led to massive data collection (corporate data, data warehouses, webs, just to name a few) and information (or...
Automatic Web-Page Classification by Using Machine Learning Methods (2001)
Makoto Tsukada, Takashi Washio, Hiroshi Motoda
Abstract. This paper describes automatic Web-page classification by using machine learning methods. Recently, the importance of portal site services is increasing including the search engine function...
An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data (2000)
Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda
. This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing substructures in a given graph data set. A graph transaction is represented...
Enhancing the Plausibility of Law Equation Discovery (2000)
Takashi Washio, Hiroshi Motoda, Yuji Niwa
After the pioneering work of the BACON system, the study in the field of scientific discovery has been directed to the discovery of more plausible law equations to represent the first principles...
Takashi Washio, Hiroshi Motoda, Yuji Niwa
Most conventional law equation discovery systems suchasBACON require experimental environments to acquire their necessary data. The mathematical techniques such as linear system identification and...
Takashi Washio, Hiroshi Motoda
The fundamental theorems of dimensional analysis are extended in terms of scale-types of measurement quantities. This extension enhances the applicabilityof the dimensional analysis to wide domains....
Discovery of first-principle equations based on scale-type-based and data-driven-reasoning (1998)
Takashi Washio, Hiroshi Motoda
We propose a novel approach to automatically discover formulae of first principles from the measurement data. The formulae obtained by our approach are ensured to reflect the first principles despite...
A Monotonic Measure for Optimal Feature Selection (1998)
Huan Liu, Hiroshi Motoda, Manoranjan Dash
. Feature selection is a problem of choosing a subset of relevant features. Researchers have been searching for optimal feature selection methods. `Branch and Bound' and Focus are two...
Feature Transformation and Subset Selection (1998)
uction and feature extraction. Both are sometimes called feature discovery. Assuming the original set consists of A 1 ; A 2 ; :::; A n features, these variants can be defined below. Feature...
Feature transformation and subset selection (1998)
As computer and database technologies constantly advance, human beings rely more and more on computers to accumulate data, process data, and make use of data. Machine learning, knowledge discovery,...
Discovery of First Principle Based on Data-Driven Reasoning (1997)
Takashi Washio, Hiroshi Motoda
this paper is to propose a new approach to automatically discover formulae of first principles. Our approach lies in between the deductive approaches as represented by dimension-based and...
Discovery of Possible LawFormulae Based on Measurement Scale (1996)
Takashi Washio, Hiroshi Motoda
: We propose a novel approach to automatically discover law formulae of first principles from the measurement data. Our approach lies in between the deductive approaches as represented by...
A Flash-Memory Based File System (1995)
Atsuo Kawaguchi, Shingo Nishioka, Hiroshi Motoda
A flash memory device driver that supports a conventional UNIX file system transparently was designed. To avoid the limitations due to flash memory's restricted number of write cycles and its...
A flash-memory based file system (1995)
Atsuo Kawaguchi, Shingo Nishioka, Hiroshi Motoda
A flash memory device driver that supports a conventional UNIX file system transparently was designed. To avoid the limitations due to flash memory's restricted number of write cycles and its...
A flash-memory based file system (1995)
Atsuo Kawaguchi, Shingo Nishioka, Hiroshi Motoda
A flash memory device driver that supports a conventional UNIX file system transparently was designed. To avoid the limitations due to flash memory's restricted number of write cycles and its...
Relevance Reasoning to Guide Compositional Modeling (1992)
Alon Y. Levy, Yumi Iwasaki, Hiroshi Motoda
The ability to choose an appropriate manner in which to model a given device is crucial in making a compositional modeling [3] approach successful. In compositional modeling, a system is provided...
CONSUMER BEHAVIOR ANALYSIS BY GRAPH MINING TECHNIQUE
KATSUTOSHI YADA, HIROSHI MOTODA, TAKASHI WASHIO, ASUKA MIYAWAKI
In this paper, we discuss how graph mining system is applied to sales transaction data so as to understand consumer behavior. First, existing research of consumer behavior analysis for sequential...