Results 1  10
of
406
gSpan: GraphBased Substructure Pattern Mining
, 2002
"... We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and ..."
Abstract

Cited by 639 (34 self)
 Add to MetaCart
We investigate new approaches for frequent graphbased pattern mining in graph datasets and propose a novel algorithm called gSpan (graphbased Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depthfirst search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.
Efficiently Mining Frequent Trees in a Forest
, 2002
"... Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semistructured data, and so on. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TreeMiner, a novel algorithm to discover all frequent subtrees ..."
Abstract

Cited by 209 (6 self)
 Add to MetaCart
(Show Context)
Mining frequent trees is very useful in domains like bioinformatics, web mining, mining semistructured data, and so on. We formulate the problem of mining (embedded) subtrees in a forest of rooted, labeled, and ordered trees. We present TreeMiner, a novel algorithm to discover all frequent subtrees in a forest, using a new data structure called scopelist. We contrast TreeMiner with a pattern matching tree mining algorithm (PatternMatcher). We conduct detailed experiments to test the performance and scalability of these methods. We find that TreeMiner outperforms the pattern matching approach by a factor of 4 to 20, and has good scaleup properties. We also present an application of tree mining to analyze real web logs for usage patterns.
Graph Indexing: A Frequent Structurebased Approach
, 2004
"... Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graphbased indices. In this paper, we investigate the is ..."
Abstract

Cited by 195 (25 self)
 Add to MetaCart
Graph has become increasingly important in modelling complicated structures and schemaless data such as proteins, chemical compounds, and XML documents. Given a graph query, it is desirable to retrieve graphs quickly from a large database via graphbased indices. In this paper, we investigate the issues of indexing graphs and propose a novel solution by applying a graph mining technique. Di#erent from the existing pathbased methods, our approach, called gIndex, makes use of frequent substructure as the basic indexing feature. Frequent substructures are ideal candidates since they explore the intrinsic characteristics of the data and are relatively stable to database updates. To reduce the size of index structure, two techniques, sizeincreasing support constraint and discriminative fragments, are introduced. Our performance study shows that gIndex has 10 times smaller index size, but achieves 310 times better performance in comparison with a typical pathbased method, GraphGrep. The gIndex approach not only provides an elegant solution to the graph indexing problem, but also demonstrates how database indexing and query processing can benefit from data mining, especially frequent pattern mining. Furthermore, the concepts developed here can be applied to indexing sequences, trees, and other complicated structures as well.
Mining sequential patterns by patterngrowth: The PrefixSpan approach
 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 194 (10 self)
 Add to MetaCart
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 methods, such as GSP, explore a candidate generationandtest 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 projectionbased, sequential patterngrowth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growthbased sequential pattern mining, FreeSpan [8], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a prioribased algorithm GSP, FreeSpan, and SPADE [29] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with userspecified constraints. The high promise of the patterngrowth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.
Efficient Mining of Frequent Subgraph in the Presence of Isomorphism
"... Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgr ..."
Abstract

Cited by 189 (23 self)
 Add to MetaCart
Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. In this paper, we propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graphical framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current startoftheart subgraph mining algorithm gSpan.
A quickstart in frequent structure mining can make a difference
 In Proc. of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD2004
, 2004
"... Given a database, structure mining algorithms search for substructures that satisfy constraints such as minimum frequency, minimum confidence, minimum interest and maximum frequency. Examples of substructures include graphs, trees and paths. For these substructures many mining algorithms have bee ..."
Abstract

Cited by 156 (5 self)
 Add to MetaCart
(Show Context)
Given a database, structure mining algorithms search for substructures that satisfy constraints such as minimum frequency, minimum confidence, minimum interest and maximum frequency. Examples of substructures include graphs, trees and paths. For these substructures many mining algorithms have been proposed. In order to make graph mining more efficient, we investigate the use of the “quickstart principle”, which is based on the fact that these classes of structures are contained in each other, thus allowing for the development of structure mining algorithms that split the search into steps of increasing complexity. We introduce the GrAph/Sequence/Tree extractiON (Gaston) algorithm that implements this idea by searching first for frequent paths, then frequent free trees and finally cyclic graphs. We investigate two alternatives for computing the frequency of structures and present experimental results to relate these alternatives.
Frequent SubStructureBased Approaches for Classifying Chemical Compounds
 In Proceedings of ICDM’03
, 2003
"... In this paper we study the problem of classifying chemical compound datasets. We present a substructurebased classification algorithm that decouples the substructure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topologi ..."
Abstract

Cited by 141 (6 self)
 Add to MetaCart
In this paper we study the problem of classifying chemical compound datasets. We present a substructurebased classification algorithm that decouples the substructure discovery process from the classification model construction and uses frequent subgraph discovery algorithms to find all topological and geometric substructures present in the dataset. The advantage of our approach is that during classification model construction, all relevant substructures are available allowing the classifier to intelligently select the most discriminating ones. The computational scalability is ensured by the use of highly efficient frequent subgraph discovery algorithms coupled with aggressive feature selection. Our experimental evaluation on eight different classification problems shows that our approach is computationally scalable and outperforms existing schemes by 10% to 35%, on the average.
Finding frequent patterns in a large sparse graph
 SIAM Data Mining Conference
, 2004
"... This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edgedisjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine ..."
Abstract

Cited by 131 (5 self)
 Add to MetaCart
This paper presents two algorithms based on the horizontal and vertical pattern discovery paradigms that find the connected subgraphs that have a sufficient number of edgedisjoint embeddings in a single large undirected labeled sparse graph. These algorithms use three different methods to determine the number of the edgedisjoint embeddings of a subgraph that are based on approximate and exact maximum independent set computations and use it to prune infrequent subgraphs. Experimental evaluation on real datasets from various domains show that both algorithms achieve good performance, scale well to sparse input graphs with more than 100,000 vertices, and significantly outperform a previously developed algorithm.
Mining Frequent Patterns in Data Streams at Multiple Time Granularities
, 2002
"... Although frequentpattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent la ..."
Abstract

Cited by 131 (8 self)
 Add to MetaCart
Although frequentpattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time.
Graph mining: laws, generators, and algorithms
 ACM COMPUT SURV (CSUR
, 2006
"... How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation in ..."
Abstract

Cited by 130 (7 self)
 Add to MetaCart
How does the Web look? How could we tell an abnormal social network from a normal one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks to sociology to biology and many more. Indeed, any M: N relation in database terminology can be represented as a graph. A lot of these questions boil down to the following: “How can we generate synthetic but realistic graphs? ” To answer this, we must first understand what patterns are common in realworld graphs and can thus be considered a mark of normality/realism. This survey give an overview of the incredible variety of work that has been done on these problems. One of our main contributions is the integration of points of view from physics, mathematics, sociology, and computer science. Further, we briefly describe recent advances on some related and interesting graph problems.