Graph mining


                    Graph mining

 Journal of Chemical Informatics (imedpub Publishing S.L) has announced almost 50 percent discount on article processing charge to commemorate.

The Editorial Board of Journal Chemical Informatics is overwhelmed by the response and eagerness of the academic and research contributors to publish with the journal and take part in the year-long celebrations. During this year we look forward to taking some initiatives that would encourage and reward our prospective audience which include clinicians, research scientists, decision-makers and a range of professionals in the healthcare community.


The growth of the use of semi-structured data has created new opportunities for data mining, which has traditionally been concerned with tabular data sets, reflecting the strong association between data mining and relational databases. Much of the world's interesting and mineable data does not easily fold into relational databases, though a generation of software engineers have been trained to believe this was the only way to handle data, and data mining algorithms have generally been developed only to cope with tabular data.


XML, being the most frequent way of representing semi-structured data, is able to represent both tabular data and arbitrary trees. Any particular representation of data to be exchanged between two applications in XML is normally described by a schema often written in XSD. Practical examples of such schemata, for instance NewsML, are normally very sophisticated, containing multiple optional subtrees, used for representing special case data. Frequently around 90% of a schema is concerned with the definition of these optional data items and sub-trees.


Messages and data, therefore, that are transmitted or encoded using XML and that conform to the same schema are liable to contain very different data depending on what is being transmitted.


Such data presents large problems for conventional data mining. Two messages that conform to the same schema may have little data in common. Building a training set from such data means that if one were to try to format it as tabular data for conventional data mining, large sections of the tables would or could be empty.

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Journal of chemical Informatics