Results 11 - 20 of 505

Mining sequential patterns by pattern-growth: The PrefixSpan approach

by Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Jianyong Wang, Helen Pinto, Qiming Chen, Umeshwar Dayal, Mei-Chun Hsu - IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING , 2004
"... Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining ..."
Abstract - Cited by 206 (10 self) - Add to MetaCart
mining methods, such as GSP, explore a candidate generation-and-test approach [1] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based

A Probabilistic Substructure-Based Approach for Graph Classification

by H. D. K. Moonesinghe, Hamed Valizadegan, Samah Fodeh, Pang-ning Tan
"... The classification of graph based objects is an important challenge from a knowledge discovery standpoint and has attracted considerable attention recently. In this paper, we present a probabilistic substructure-based approach for classifying a graphbased dataset. More specifically, we use a frequen ..."
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frequent subgraph mining algorithm to construct substructure based descriptors and apply the maximum entropy principle to convert the local patterns into a global classification model for graph data. Empirical studies conducted on real world data sets showed that the maximum entropy substructure-based

Mining for Structural Anomalies in Graph-based Data

by William Eberle, Lawrence Holder , 2007
"... In this paper we present graph-based approaches to mining for anomalies in domains where the anomalies consist of unexpected entity/relationship alterations that closely resemble non-anomalous behavior. We introduce three novel algorithms for the purpose of detecting anomalies in all possible types ..."
Abstract - Cited by 5 (0 self) - Add to MetaCart
In this paper we present graph-based approaches to mining for anomalies in domains where the anomalies consist of unexpected entity/relationship alterations that closely resemble non-anomalous behavior. We introduce three novel algorithms for the purpose of detecting anomalies in all possible types

Finding Frequent Substructures in Chemical Compounds

by Luc Dehaspe, Hannu Toivonen, Ross Donald King , 1998
"... The discovery of the relationships between chemical structure and biological function is central to biological science and medicine. In this paper we apply data mining to the problem of predicting chemical carcinogenicity. This toxicology application was launched at IJCAI'97 as a research chall ..."
Abstract - Cited by 133 (11 self) - Add to MetaCart
challenge for artificial intelligence. Our approach to the problem is descriptive rather than based on classification; the goal being to find common substructures and properties in chemical compounds, and in this way to contribute to scientific insight. This approach contrasts with previous machine learning

Graph-based data mining for social network analysis

by Maitrayee Mukherjee - In Proceedings of the ACM KDD Workshop on Link Analysis and Group Detection , 2004
"... In this research, we compare and contrast the salient features of illicit group information with legitimate group data. We describe how the graph-based knowledge discovery system, SUBDUE, when run in unsupervised discovery mode, finds structural patterns embedded within social network data. We also ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
In this research, we compare and contrast the salient features of illicit group information with legitimate group data. We describe how the graph-based knowledge discovery system, SUBDUE, when run in unsupervised discovery mode, finds structural patterns embedded within social network data. We also

Graph-based Data Mining in Epidemia and Terrorism Data

by Courtney D Corley, Diane Cook, Lawrence B. Holder, Karan P. Singh
"... Graph-based data mining (GDM) is the task of finding novel, useful, and understandable graph-theoretic patterns in a graph representation of data. Our approach to graph-based data mining, Subdue, focuses on identifying novel, but not necessarily most frequent, patterns in a graph representation of d ..."
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Graph-based data mining (GDM) is the task of finding novel, useful, and understandable graph-theoretic patterns in a graph representation of data. Our approach to graph-based data mining, Subdue, focuses on identifying novel, but not necessarily most frequent, patterns in a graph representation

Discovering structural anomalies in graph-based data

by William Eberle, Lawrence Holder - In Proceedings of the Seventh IEEE International Conference on Data Mining Workshops , 2007
"... The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, particularly for fraud, but less work has been done in terms of detecting anomalies in graph-based data. While th ..."
Abstract - Cited by 27 (0 self) - Add to MetaCart
The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns. One important area of data mining is anomaly detection, particularly for fraud, but less work has been done in terms of detecting anomalies in graph-based data. While

Discovering Frequent Substructures In Large Unordered Trees

by Tatsuya Asai, Hiroki Arimura, Takeaki Uno, Shin-ichi Nakano - IN PROC. OF THE 6TH INTL. CONF. ON DISCOVERY SCIENCE , 2003
"... In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semi-structured data, where both of the patterns and the data are modeled by labeled unordered trees. An unordered tree is a directed acyclic graph with a specified node called the root, ..."
Abstract - Cited by 51 (6 self) - Add to MetaCart
In this paper, we study a data mining problem of discovering frequent substructures in a large collection of semi-structured data, where both of the patterns and the data are modeled by labeled unordered trees. An unordered tree is a directed acyclic graph with a specified node called the root

Weighted Substructure Mining for Image Analysis

by Sebastian Nowozin, Koji Tsuda, Takeaki Uno, Gökhan Bakır - In CVPR , 2007
"... In web-related applications of image categorization, it is desirable to derive an interpretable classification rule with high accuracy. Using the bag-of-words representation and the linear support vector machine, one can partly fulfill the goal, but the accuracy of linear classifiers is not high and ..."
Abstract - Cited by 39 (6 self) - Add to MetaCart
next step, each image is represented as a graph where nodes correspond to local image features and edges encode geometric relations between features. Combining graph mining and boosting, we can obtain a classification rule based on subgraph features that contain more information than the set features

Graph-based relational learning: Current and future directions

by Lawrence B. Holder, Diane J. Cook - ACM SIGKDD Explorations
"... Graph-based relational learning (GBRL) differs from logicbased relational learning, as addressed by inductive logic programming techniques, and differs from frequent subgraph discovery, as addressed by many graph-based data mining techniques. Learning from graphs, rather than logic, presents represe ..."
Abstract - Cited by 14 (5 self) - Add to MetaCart
representational issues both in input data preparation and output pattern language. While a form of graph-based data mining, GBRL focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data. This approach to graph-based data mining provides both simplifications
Results 11 - 20 of 505