Hiroshi Motoda

Top 10 algorithms in data mining

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, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

Feature Selection for Knowledge Discovery and Data Mining

From the Publisher: With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the humans capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in laymans terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines for how to use different methods under various circumstances and points out new challenges in this exciting area of research. Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery, and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.

An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

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 by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem.

Feature Extraction, Construction and Selection: A Data Mining Perspective

From the Publisher: The book can be used by researchers and graduate students in machine learning, data mining, and knowledge discovery, who wish to understand techniques of feature extraction, construction and selection for data pre-processing and to solve large size, real-world problems. The book can also serve as a reference book for those who are conducting research about feature extraction, construction and selection, and are ready to meet the exciting challenges ahead of us.

State of the art of graph-based data mining

The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.

Complete Mining of Frequent Patterns from Graphs: Mining Graph Data

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 reality, there are many applications where data are described in a more structural way, e.g. chemical compounds and Web browsing history. There are a few approaches that can discover characteristic patterns from graph-structured data in the field of machine learning. However, almost all of them are not suitable for such applications that require a complete search for all frequent subgraph patterns in the data. In this paper, we propose a novel principle and its algorithm that derive the characteristic patterns which frequently appear in graph-structured data. Our algorithm can derive all frequent induced subgraphs from both directed and undirected graph structured data having loops (including self-loops) with labeled or unlabeled nodes and links. Its performance is evaluated through the applications to Web browsing pattern analysis and chemical carcinogenesis analysis.

Knowledge acquisition for knowledge-based systems

The work reported at the first Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop is discussed, providing both an overview of the field and an introduction to a series of articles on knowledge acquisition. The discussion covers tools, methods, and mediating representations; real-time problem solving; the system-model-operator metaphor; an interview architecture based on dynamic analysis, inductive knowledge acquisition from structured data; research in Japan; how to make application programming easier; justification-based knowledge acquisition; integrating knowledge acquisition and performance systems; tasks, methods, and knowledge; rule induction; hypertext; explanation-based learning and case-based reasoning; and interviewing.<<ETX>>

Rough Sets and Intelligent Systems Paradigms

Second International Conference, RSEISP 2014, Held as Part of JRS 2014, Granada and Madrid, Spain, July 9-13, 2014. Proceedings

Instance Selection and Construction for Data Mining

The ability to analyze and understand massive data sets lags far behind the ability to gather and store the data. To meet this challenge, knowledge discovery and data mining (KDD) is growing rapidly as an emerging field. However, no matter how powerful computers are now or will be in the future, KDD researchers and practitioners must consider how to manage ever-growing data which is, ironically, due to the extensive use of computers and ease of data collection with computers. Many different approaches have been used to address the data explosion issue, such as algorithm scale-up and data reduction. Instance, example, or tuple selection pertains to methods or algorithms that select or search for a representative portion of data that can fulfill a KDD task as if the whole data is used. Instance selection is directly related to data reduction and becomes increasingly important in many KDD applications due to the need for processing efficiency and/or storage efficiency. One of the major means of instance selection is sampling whereby a sample is selected for testing and analysis, and randomness is a key element in the process. Instance selection also covers methods that require search. Examples can be found in density estimation (finding the representative instances -- data points -- for a cluster); boundary hunting (finding the critical instances to form boundaries to differentiate data points of different classes); and data squashing (producing weighted new data with equivalent sufficient statistics). Other important issues related to instance selection extend to unwanted precision, focusing, concept drifts, noise/outlier removal, data smoothing, etc. Instance Selection and Construction for Data Mining brings researchers and practitioners together to report new developments and applications, to share hard-learned experiences in order to avoid similar pitfalls, and to shed light on the future development of instance selection. This volume serves as a comprehensive reference for graduate students, practitioners and researchers in KDD.

On Issues of Instance Selection

The digital technologies and computer advances with the booming internet uses have ledto massive data collection (corporate data, data warehouses, webs, just to name a few) andinformation (or misinformation) explosion. Szalay and Gray described this phenomenon as“drowning in data” (Szalay and Gray, 1999). They reported that each year the detectors attheCERNparticlecolliderinSwitzerlandrecord1petabyteofdata;andresearchersinareasof science from astronomy to the human genome are facing the same problems and chokingon information. A very natural question is “now that we have gathered so much data, whatdo we do with it?” Raw data is rarely of direct use and manual analysis simply cannotkeep pace with the fast growth of data. Data mining and knowledge discovery (KDD), as anew emerging field comprising disciplines such as databases, statistics, machine learning,comes to the rescue. KDD attempts to turn raw data into nuggets and create special edgesin this ever competitive world for science discovery and business intelligence.TheKDDprocessisdefinedinFayyadetal.(1996)as