Effective pattern discovery for text mining
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Effective pattern discovery for text mining

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this is the part of effective pattern discovery for text mining

this is the part of effective pattern discovery for text mining
till system specification this PPT is prepared by Deexith and presented by me and my team mates

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Effective pattern discovery for text mining Effective pattern discovery for text mining Presentation Transcript

  • Effective Pattern Discovery For Text Mining Guide : Mr. S.Rajesh
  • Abstract • • •
  • EXISTING SYSTEM
  • Problems in Existing System
  • PROPOSED SYSTEM • We provide an effective pattern discovery technique, which first calculates discovered specificities of patterns and then evaluates term weights according to the distribution of terms in the discovered patterns rather than the distribution in documents for solving the misinterpretation problem. • The proposed approach can improve the accuracy of evaluating term weights because discovered patterns are more specific than whole documents. • We also conduct numerous experiments on the latest data collection, Reuters Corpus Volume 1 (RCV1) and Text Retrieval Conference (TREC) filtering topics, to evaluate the proposed technique.
  • System Specifications Hardware Requirements: System Hard Disk Floppy Drive Monitor Mouse RAM Keyboard : : : : : : : Pentium IV 2.4 GHz. 40 GB. 1.44 Mb. 14’ Colour Monitor. Optical Mouse. 512 Mb. 101 Keyboard. Software Requirements: Operating system : Windows XP and IIS Coding Language : ASP .NET with C# SP1 Data Base : SQL Server 2005.
  • Conclusion Many data mining techniques have been proposed in the last decade. These techniques include association rule mining, frequent item set mining, sequential pattern mining, maximum pattern mining, and closed pattern mining. However, using these discovered knowledge (or patterns) in the field of text mining is difficult and ineffective. The reason is that some useful long patterns with high specificity lack in support (i.e., the lowfrequency problem). We argue that not all frequent short patterns are useful. Hence, misinterpretations of patterns derived from data mining techniques lead to the ineffective performance. In this research work, an effective pattern discovery technique has been proposed to overcome the low-frequency and misinterpretation problems for text mining. The proposed technique uses two processes, pattern deploying and pattern evolving, to refine the discovered patterns in text documents. The experimental results show that the proposed model outperforms not only other pure data mining-based methods and the concept based model, but also term-based state-of-the-art models, such as BM25 and SVM-based models.