The XML Data Mining Research Based on the Multi-Level Technology

XML has become the standard form of data exchange, more and more data in this form for storage, implying a lot of knowledge in these data information, the need for data mining processing. For XML data mining method at present, most of the need is to pass the XML data into relational data pretreatment process, using the traditional method for processing, data mining process is complex and the effect is not ideal. Therefore, there is an urgent need some effective methods for XML data mining directly.

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Research on XML Data Mining Model Based on Multi-Level Technology

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1574 ◽

2013 ◽

Vol 846-847 ◽

pp. 1574-1577

Author(s):

Jian Xin Zhu

Keyword(s):

Data Mining ◽

Web 2.0 ◽

Basic Problem ◽

Mining Method ◽

Xml Data ◽

Research Issues ◽

Level Data ◽

Multi Level ◽

Mining Model ◽

Xml Technique

The era of Web 2.0 has been coming, and more and more Web 2.0 application, such social networks and Wikipedia, have come up. As an industrial standard of the Web 2.0, the XML technique has also attracted more and more researchers. However, how to mine value information from massive XML documents is still in its infancy. In this paper, we study the basic problem of XML data mining-XML data mining model. We design a multi-level XML data mining model, propose a multi-level data mining method, and list some research issues in the implementation of XML data mining systems.

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Data Mining on XML Data

Encyclopedia of Data Warehousing and Mining, Second Edition ◽

10.4018/978-1-60566-010-3.ch079 ◽

2011 ◽

pp. 506-510

Author(s):

Qin Ding

Keyword(s):

Data Mining ◽

Data Storage ◽

Efficient Algorithms ◽

Relational Data ◽

Xml Data ◽

Structure Information ◽

Data Mining Algorithms ◽

Mining Algorithms ◽

Naive Approach

With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.

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Key Data Optimization Mining in Massive Student Database Model

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.687-691.1466 ◽

2014 ◽

Vol 687-691 ◽

pp. 1466-1469

Author(s):

Zhen Chao Wang

Keyword(s):

Data Mining ◽

Traditional Method ◽

Experimental Results ◽

Redundant Information ◽

Mining Method ◽

Information Mining ◽

Database Model ◽

Data Mining Method ◽

Mining Model ◽

Effective Cluster

In the process of massive student data mining using traditional method, special words and related characteristics were used as mining objects. The concealment and feature of deliberately camouflaged of information made it is difficult for mining model to form an effective cluster centers, which reduced the accuracy of information mining. Hence an optimized data mining method was proposed. According to the degree of generalization and fuzziness of the feature words of student, the threshold of mining information was set, which avoided the effects of redundant information, thus the efficiency of mining was improved. The experimental results showed that using the improved algorithm to perform information mining in massive student database could effectively improve mining efficiency.

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