ABSTRACT
In this digital era most of the information is made available in digital form. For many years, people have held the hypothesis that using phrases for are presentation of document and topic should perform better than terms. In this paper we are examine and investigate this fact with considering several state of art data mining methods that gives satisfactory results to improve the effectiveness of the pattern. Here we implementing pattern detection method to solve problem of term-based methods and improved result which helpful in information retrieval systems. Our proposal is also evaluated for several well distinguish domain, offering in all cases, reliable taxonomies considering precision and recall along with F-measure. For the experiment, we use Reuters
(RCV1) dataset and the results show that we improve the discovering pattern as compared to previous text mining methods. The results of the experiment setup show that the keyword-based methods not give better performance than patternbased method. The results also indicate that removal of meaningless patterns not only reduces the cost of computation but also improves the effectiveness of the system

EXISTING SYSTEM:
With the development of World Wide Web, web search engines have contributed a lot in searching information from the web. They help in finding information on the web quick and easy. But there is still room for improvement. Current web search engines do not In
Data selection generating a target dataset and selecting a dataset or a subset of large data sources where discovery is to be performed.
Disadvantage:
1) Phrases have inferior statistical properties to words
(2) They have a low frequency of occurrence
(3) There are a large number of redundant and noisy phrases among them
PROPOSED SYSTEM:
Pre-processing process involves data cleaning and noise removing. It also includes collecting required information from selected data fields, Here we implementing pattern detection method to solve problem of term-based methods and improved result which helpful in information retrieval systems To solve disadvantage of A priori like algorithm, number of new algorithms has been proposed
.
Advantage:
Personalized web search is considered as a promising solution to handle these problems .The results also indicate that removal of meaningless patterns
Different search results can be provided depending upon the choice and information needs of users involves data cleaning and noise removing.
They have a high frequency of occurrence.

PROBLEM STATEMENT:
The phrase-based indexing language was not superior to the word-based one.
Although phrases carry less ambiguous and more concise meanings than individual words, the likely reasons for the depressing performance from the use of phrases are phrases have inferior statistical properties to words they have a low frequency of occurrence, and there are a large number of redundant and noisy phrases among them . The remainder of this paper is organized as follows.
Section gives a detailed overview of the PTM model highlights the related problems and definitions with the PTM model. Moreover it is also time consuming. Personalized web search is considered as a promising solution to handle these problems, since different search results can be provided depending upon the choice and information needs of users. It exploits user information and search context to learning in which sense a query refer.
SCOPE:
Here we focused on experimental setup of our alternating approaches to the pattern taxonomy model to Enhanced performance of pattern deploying model
(EPDM). To implement the method three aspects are discussed including experimental Datasets, Performance measures and Evaluation procedures. The latest version of Reuter’s document collection is chosen among several versions as our benchmark dataset. Here we get RCV1 cd’s from NIST, they made available data set for research purposes.

MODULE DESCRIPTION:
Number of Modules
After careful analysis the system has been identified to have the following modules:
1.
Data mining Module.
2.
Keyword Based Information Retrieval Module.
3.
Phrase Based Information Retrieval Module.
4.
Sequential pattern mining Module.
1.Data mining Module:
Text mining is a variation on a field called data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
2. Keyword Based Information Retrieval Module:
Text mining is the technique that helps users finds useful information from a large amount of digital text data. Many text-mining methods have been developed in order to achieve the goal of retrieving useful information for users

Most text mining methods use the keyword-based approaches, whereas others choose the phrase technique to construct a text representation for a set of documents.
The keywords entered by users may imply different information needs of different users, and this causes ambiguity during query processing. Liu and
Chen discuss several post-processing methods for keyword-based retrieval on structured data with the objective of making the results more meaningful to users. Keyword-based retrieval is well studied in the context of information retrieval.
3. Phrase Based Information Retrieval Module:
An information retrieval system uses phrases to index, retrieve, organize and describe documents. Phrases are identified that predict the presence of other phrases in documents. Documents are the indexed according to their included phrases. Related phrases and phrase extensions are also identified. Phrases in a query are identified and used to retrieve and rank documents. Phrases are also used to cluster documents in the search results, create document descriptions, and eliminate duplicate documents from the search results, and from the index.
4
. Sequential pattern mining Module:
Sequential Pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and

thus time series mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of structured data mining.
There are several key traditional computational problems addressed within this field. These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for similarity, and recovering missing sequence members.
SOFTWARE REQUIREMENTS:
Operating System
Technology
Web Technologies
IDE
: Windows
: Java and J2EE
: Html, JavaScript, CSS
: My Eclipse
Web Server : Tomcat
Tool kit : Android Phone
Database
Java Version
: My SQL
: J2SDK1.5
HARDWARE REQUIREMENTS:
Hardware : Pentium
Speed : 1.1 GHz
RAM : 1GB
Hard Disk : 20 GB
Floppy Drive : 1.44 MB

Key Board : Standard Windows Keyboard
Mouse : Two or Three Button Mouse
Monitor : SVGA