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21
An efficient algorithm for discovering frequent subgraphs
- IEEE Transactions on Knowledge and Data Engineering
, 2002
"... Abstract — Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in various application areas. However, as data mining techniques are being increasingly applied to non-traditional domains, existing frequent pattern discovery approach cannot be used. This i ..."
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Cited by 120 (7 self)
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Abstract — Over the years, frequent itemset discovery algorithms have been used to find interesting patterns in various application areas. However, as data mining techniques are being increasingly applied to non-traditional domains, existing frequent pattern discovery approach cannot be used. This is because the transaction framework that is assumed by these algorithms cannot be used to effectively model the datasets in these domains. An alternate way of modeling the objects in these datasets is to represent them using graphs. Within that model, one way of formulating the frequent pattern discovery problem is as that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally efficient algorithm, called FSG, for finding all frequent subgraphs in large graph datasets. We experimentally evaluate the performance of FSG using a variety of real and synthetic datasets. Our results show that despite the underlying complexity associated with frequent subgraph discovery, FSG is effective in finding all frequently occurring subgraphs in datasets containing over 200,000 graph transactions and scales linearly with respect to the size of the dataset. Index Terms — Data mining, scientific datasets, frequent pattern discovery, chemical compound datasets.
Efficient data mining for maximal frequent subtrees
- In ICDM ’03: Proceedings of the Third IEEE International Conference on Data Mining
, 2003
"... A new type of tree mining is defined in this paper, which uncovers maximal frequent induced subtrees from a database of unordered labeled trees. A novel algorithm, PathJoin, is proposed. The algorithm uses a compact data structure, FST-Forest, which compresses the trees and still keeps the original ..."
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Cited by 43 (0 self)
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A new type of tree mining is defined in this paper, which uncovers maximal frequent induced subtrees from a database of unordered labeled trees. A novel algorithm, PathJoin, is proposed. The algorithm uses a compact data structure, FST-Forest, which compresses the trees and still keeps the original tree structure. PathJoin generates candi-date subtrees by joining the frequent paths in FST-Forest. Such candidate subtree generation is localized and thus substantially reduces the number of candidate subtrees. Ex-periments with synthetic data sets show that the algorithm is effective and efficient. 1
Discovering Frequent Geometric Subgraphs
- In IEEE Intl. Conference on Data Mining ’02
, 2002
"... As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the ..."
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Cited by 38 (1 self)
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As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. In this paper we present a computationally e#cient algorithm for finding frequent geometric subgraphs in a large collection of geometric graphs. Our algorithm is able to discover geometric subgraphs that can be rotation, scaling and translation invariant, and it can accommodate inherent errors on the coordinates of the vertices. We evaluated the performance of the algorithm using a large database of over 20,000 real two dimensional chemical structures, and our experimental results show that our algorithms requires relatively little time, can accommodate low support values, and scales linearly on the number of transactions.
OntoMiner: Bootstrapping and populating ontologies from domain specific Web sites
- In First Workshop on Semantic Web and Databases (SWDB
, 2003
"... Abstract. RDF/XML has been widely recognized as the standard for annotating online Web documents and for transforming the HTML Web to the so called Semantic Web. In order to enable widespread usability for the Semantic Web there is a need to bootstrap large, rich and up-todate domain ontologies that ..."
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Cited by 34 (2 self)
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Abstract. RDF/XML has been widely recognized as the standard for annotating online Web documents and for transforming the HTML Web to the so called Semantic Web. In order to enable widespread usability for the Semantic Web there is a need to bootstrap large, rich and up-todate domain ontologies that organize most relevant concepts, their relationships and instances. In this paper, we present automated techiques for bootstrapping and populating specialized domain ontologies by organizing and mining a set of relevant Web sites provided by the user. We develop algorithms that detect and utilize HTML regularities in the Web documents to turn them into hierarchical semantic structures encoded as XML. Next, we present tree-mining algorithms that identify key domain concepts and their taxonomical relationships. We also extract semistructed concept instances annotated with their labels whenever they are available. Experimental evaluation for the News and Hotels domain indicates that our algorithms can bootstrap and populate domain specific ontologies with high precision and recall. 1
WISDOM: Web intrapage informative structure mining based on document object model
- IEEE TKDE
, 2005
"... To increase the commercial value and accessibility of pages, most content sites tend to publish their pages with intra-site redundant information, such as navigation panels, advertisements and copyright announcements. Such redundant information increases the index size of general search engines and ..."
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Cited by 17 (0 self)
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To increase the commercial value and accessibility of pages, most content sites tend to publish their pages with intra-site redundant information, such as navigation panels, advertisements and copyright announcements. Such redundant information increases the index size of general search engines and causes page topics to drift. In this paper, we study the problem of mining intra-page informative structure in news Web sites in order to find and eliminate redundant information. Note that intra-page informative structure is a sub-set of the original Web page and is composed of a set of fine-grained and informative blocks. The intra-page informative structures of pages in a news Web site contain only anchors linking to news pages or bodies of news articles. We propose an intra-page informative structure mining system called WISDOM (Web Intra-page Informative Structure Mining based on the Document Object Model) which applies Information Theory to DOM tree knowledge in order to build the structure. WISDOM splits a DOM tree into many small sub-trees and applies a top-down informative block searching algorithm to select a set of candidate informative blocks. The structure is built by expanding the set using proposed merging methods. Experiments on several real news Web sites show high precision and recall rates which validates WISDOM’s practical applicability.
Online Algorithms for Mining Semi-structured Data Stream
- IN PROC. 2002 INT. CONF. ON DATA MINING (ICDM’02
, 2002
"... In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in ..."
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Cited by 16 (4 self)
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In this paper, we study an online data mining problem from streams of semi-structured data such as XML data. Modeling semi-structured data and patterns as labeled ordered trees, we present an online algorithm StreamT that receives fragments of an unseen possibly infinite semi-structured data in the document order through a data stream, and can return the current set of frequent patterns immediately on request at any time. A crucial part of our algorithm is the incremental maintenance of the occurrences of possibly frequent patterns using a tree sweeping technique. We give modifications of the algorithm to other online mining model. We present theoretical and empirical analyses to evaluate the performance of the algorithm.
SEuS: Structure extraction using summaries
- In Proc. of the 5th International Conference on Discovery Science
, 2002
"... shayan,chaw¡ Abstract. We study the problem of finding frequent structures in semistructured data (represented as a directed labeled graph). Frequent structures are graphs that are isomorphic to a large number of subgraphs in the data graph. Frequent structures form building blocks for visual explor ..."
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Cited by 15 (2 self)
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shayan,chaw¡ Abstract. We study the problem of finding frequent structures in semistructured data (represented as a directed labeled graph). Frequent structures are graphs that are isomorphic to a large number of subgraphs in the data graph. Frequent structures form building blocks for visual exploration and data mining of semistructured data. We overcome the inherent computational complexity of the problem by using a summary data structure to prune the search space and to provide interactive feedback. We present an experimental study of our methods operating on real datasets. The implementation of our methods is capable of operating on datasets that are two to three orders of magnitude larger than those described in prior work. 1
Mining XML-Enabled Association Rule with Templates
- In Proceedings of KDID04
, 2004
"... Abstract. XML-enabled association rule framework [8] extends the notion of associated items to XML fragments to present associations among trees rather than simple-structured items of atomic values. They are more flexible and powerful in representing both simple and complex structured association re ..."
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Cited by 8 (2 self)
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Abstract. XML-enabled association rule framework [8] extends the notion of associated items to XML fragments to present associations among trees rather than simple-structured items of atomic values. They are more flexible and powerful in representing both simple and complex structured association relationships inherent in XML data. Compared with traditional association mining in the well-structured world, mining from XML data, however, is confronted with more challenges due to the inherent flexibilities of XML in both structure and semantics. The primary challenges include 1) a more complicated hierarchical data structure; 2) an ordered data context; and 3) a much bigger data size. In order to make XML-enabled association rule mining truly practical and computationally tractable, in this study, we present a template model to help users specify the interesting XML-enabled associations to be mined. Techniques for template-guided mining of association rules from large XML data are also described in the paper. We demonstrate the effectiveness of these techniques through a set of experiments on both synthetic and real-life data. 1
Frequent Subgraph Mining on a Single Large Graph Using Sampling Techniques
"... Frequent subgraph mining has always been an important issue in data mining. Several frequent graph mining methods have been developed for mining graph transactions. However, these methods become less usable when the dataset is a single large graph. Also, when the graph is too large to fit in main me ..."
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Cited by 7 (1 self)
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Frequent subgraph mining has always been an important issue in data mining. Several frequent graph mining methods have been developed for mining graph transactions. However, these methods become less usable when the dataset is a single large graph. Also, when the graph is too large to fit in main memory, alternative techniques are necessary to efficiently find frequent subgraphs. We investigate the task of frequent subgraph mining on a single large graph using sampling approaches and find that sampling is a feasible approach for this task. We evaluate different sampling methods and provide a novel sampling method called 'random areas selection sampling', which produces better results than all the current graph sampling approaches with customized parameters.
Cheedella V: Monkey: Approximate Graph Mining Based on Spanning Trees
- Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference
"... In the recent past, many exact graph mining algorithms have been developed to find frequent patterns in a graph database. However, many networks or graphs generated from biological data and other applications may be incom-plete or inaccurate. Hence, it is necessary to design approx-imate graph minin ..."
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Cited by 7 (1 self)
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In the recent past, many exact graph mining algorithms have been developed to find frequent patterns in a graph database. However, many networks or graphs generated from biological data and other applications may be incom-plete or inaccurate. Hence, it is necessary to design approx-imate graph mining techniques. In this paper, we will study the problem of approximate graph mining and propose an optimized solution which uses frequent trees and a spanning tree based pre-verification check in the mining process. 1
