Mining Association Rules from XML Data

Microcomputer Applications, 2004

In recent years XML has became very popular for representing semistructured data and a standard for data exchange over the web. Mining XML data from the web is becoming increasingly important. Several encouraging attempts at developing methods for mining XML data have been proposed. However, efficiency and simplicity are still a barrier for further development. Normally, pre-processing or post-processing are required for mining XML data, such as transforming the data from XML format to relational format. In this paper, we show that extracting association rules from XML documents without any preprocessing or post-processing using XQuery is possible and analyze the XQuery implementation of the well-known Apriori algorithm. In addition, we suggest features that need to be added into XQuery in order to make the implementation of the Apriori algorithm more efficient.

2008

The inherent flexibilities of XML in both structure and semantics makes mining from XML data a complex task with more challenges compared to traditional association rule mining in relational databases. In this paper, we propose a new model for the effective extraction of generalized association rules form a XML document collection. We directly use frequent subtree mining techniques in the discovery process and do not ignore the tree structure of data in the final rules. The frequent subtrees based on the user provided support are split to complement subtrees to form the rules. We explain our model within multi-steps from data preparation to rule generation.

2009

Abstract The increasing amount of very large XML datasets available to casual users is a most challenging problem for our community, and calls for an appropriate support to efficiently gather knowledge from these data. Data mining, already widely applied to extract frequent correlations of values from both structured and semi-structured datasets, is the appropriate tool for knowledge elicitation. In this work we describe an approach to extract Tree-based association rules from XML documents.

14th IEEE International Conference on Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings., 2002

The recent success of XML as a standard to represent semi-structured data, and the increasing amount of available XML data, pose new challenges to the data mining community. In this paper we present the XMINE operator a tool we developed to extract XML association rules for XML documents. The operator, that is based on XPath and inspired by the syntax of XQuery, allows us to express complex mining tasks, compactly and intuitively. XMINE can be used to specify indifferently (and simultaneously) mining tasks both on the content and on the structure of the data, since the distinction in XML is slight.

2012

XML has become the standard for data representation on the internet. This expansion in reputation has prompt the need for a technique to access XML documents for particular information and to manipulate repositories of documents represented in XML to find specific documents. Having the ability to extract information from XML data would answer the problem of mining the web contents which is a very useful and required power nowadays. Efforts are made to develop a new tool or method for extracting information from XML data directly without any preprocessing or post processing of the XML documents. Association rules express the probability of the existing of a set of items when another set of items exists. It searches for similarities among large database. “Web mining” refer to how we can apply the traditional mining techniques that works on relational data and bind it to new data input represented in XML data which might be semi structure or unstructured. There are several techniques t...

Database and Expert Systems …, 2003

With the sheer amount of data stored, presented and exchanged using XML nowadays, the ability to extract knowledge from XML data sources becomes increasingly important and desirable. This paper aims to integrate the newly emerging XML technology with data mining technology, using association rule mining as a case in point. Compared with traditional association mining in the well-structured world (e.g., relational databases), mining from XML data is faced 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. To tackle these challenges, in this paper, we propose an extended XML-enabled association rule framework, which is flexible and powerful enough to represent both simple and complex structured association relationships inherent in XML data.

2009

Abstract The role of the eXtensible Markup Language (XML) is becoming very important in the research fields focusing on the representation, the exchange, and the integration of information coming from different data sources and containing information related to various contexts such as, for example, medical and biological data.

International Conference on Tools with Artificial Intelligence, 2000

The recent success of XML as a standard to represent semi-structured data, and the increasing amount of avail- able XML data, pose new challenges to the data mining community. In this paper we present the XMINE operator a tool we developed to extract XML association rules for XML documents. The operator, that is based on XPath and inspired by the

Revealing issues with current framework is itself a critical assignment. A review taken out for revealing issues related with Association standard mining on XML data. Preparatory essential ideas of Association rule mining is given in this work. Mining enormous amount of data, association rule mining have been demonstrated a powerful idea. Amid late years, the vast majority of the overall information exchanges are finished with XML (eXtensible Markup Language). Numerous empowering techniques have been distinguished and produced for mining XML data. In this paper, the idea of XML data examination is compressed and its importance towards association rule extraction has been represented. We have cantered a variety of strategies and methodologies of the examination, which are useful and set apart as the imperative field of XML data investigation. This work gives a study of different association rule strategies connected effectively on XML information since last one decade.

TJPRC, 2013

Data Mining refers to extracting or ―Mining‖ knowledge from large amounts of data. Today’s Industrial scenario is having manifold of data which is data rich and information poor .The information and knowledge gained can be used for applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Data Mining can be viewed as a result of natural evolution of information technology.