A Survey to Fix the Threshold and Implementation for Detecting Duplicate Web Documents

The drastic development in the information accessible on the World Wide Web has made the employment of automated tools to locate the information resources of interest, and for tracking and analyzing the same a certainty. Web Mining is the branch of data mining that deals with the analysis of World Wide Web. The concepts from various areas such as Data Mining, Internet technology and World Wide Web, and recently, Semantic Web can be said as the origin of web mining. Web mining can be defined as the procedure of determining hidden yet potentially beneficial knowledge from the data accessible in the web. Web mining comprise the sub areas: web content mining, web structure mining, and web usage mining. Web content mining is the process of mining knowledge from the web pages besides other web objects. The process of mining knowledge about the link structure linking web pages and some other web objects is defined as Web structure mining. Web usage mining is defined as the process of mining the usage patterns created by the users accessing the web pages. The search engine technology has led to the development of World Wide. The search engines are the chief gateways for access of information in the web. The ability to locate contents of particular interest amidst a huge heap has turned businesses beneficial and productive. The search engines respond to the queries by employing the process of web crawling that populates an indexed repository of web pages. The programs construct a confined repository of the segment of the web that they visit by navigating the web graph and retrieving pages. There are two main types of crawling, namely, Generic and Focused crawling. Generic crawlers crawls documents and links of diverse topics. Focused crawlers limit the number of pages with the aid of some prior obtained specialized knowledge. The systems that index, mine, and otherwise analyze pages (such as, the search engines) are provided with inputs from the repositories of web pages built by the web crawlers. The drastic development of the Internet and the growing necessity to incorporate heterogeneous data is accompanied by the issue of the existence of near duplicate data. Even if the near duplicate data don’t exhibit bit wise identical nature they are remarkably similar. The duplicate and near duplicate web pages either increase the index storage space or slow down or increase the serving costs which annoy the users, thus causing huge problems for the web search engines. Hence it is inevitable to design algorithms to detect such pages

A Deterministic Eviction Model for Removing Redundancies in Video Corpus

The traditional storage approaches are being challenged by huge data volumes. In multimedia content, every file does not necessarily get tagged as an exact duplicate; rather they are prone to editing and resulting in similar copies of the same file. This paper proposes the similarity-based deduplication approach to evict similar duplicates from the archive storage, which compares the samples of binary hashes to identify the duplicates. This eviction is done by initially dividing the query video into dynamic key frames based on the video length. Binary hash codes of these frames are then compared with existing key frames to identify the differences. The similarity score is determined based on these differences, which decides the eradication strategy of duplicate copy. Duplicate elimination goes through two levels, namely removal of exact duplicates and similar duplicates. The proposed approach has shortened the comparison window by comparing only the candidate hash codes based on the dynamic key frames and aims the accurate lossless duplicate removals. The presented work is executed and tested on the produced synthetic video dataset. Results show the reduction in redundant data and increase in the storage space. Binary hashes and similarity scores contributed to achieving good deduplication ratio and overall performance

Efficient and Robust Detection of Duplicate Videos in a Database

In this paper, the duplicate detection method is to retrieve the best matching model video for a given query video using fingerprint. We have used the Color Layout Descriptor method and Opponent Color Space to extract feature from frame and perform k-means based clustering to generate fingerprints which are further encoded by Vector Quantization. The model-to-query video distance is computed using a new distance measure to find the similarity. To perform efficient search coarse-to-fine matching scheme is used to retrieve best match. We perform experiments on query videos and real time video with an average duration of 60 sec; the duplicate video is detected with high similarity

Cleaning Web pages for effective Web content mining.

Web pages usually contain many noisy blocks, such as advertisements, navigation bar, copyright notice and so on. These noisy blocks can seriously affect web content mining because contents contained in noise blocks are irrelevant to the main content of the web page. Eliminating noisy blocks before performing web content mining is very important for improving mining accuracy and efficiency. A few existing approaches detect noisy blocks with exact same contents, but are weak in detecting near-duplicate blocks, such as navigation bars. In this thesis, given a collection of web pages in a web site, a new system, WebPageCleaner, which eliminates noisy blocks from these web pages so as to improve the accuracy and efficiency of web content mining, is proposed. WebPageCleaner detects both noisy blocks with exact same contents as well as those with near-duplicate contents. It is based on the observation that noisy blocks usually share common contents, and appear frequently on a given web site. WebPageCleaner consists of three modules: block extraction, block importance retrieval, and cleaned files generation. A vision-based technique is employed for extracting blocks from web pages. Blocks get their importance degree according to their block features such as block position, and level of similarity of block contents to each other. A collection of cleaned files with high importance degree are generated finally and used for web content mining. The proposed technique is evaluated using Naive Bayes text classification. Experiments show that WebPageCleaner is able to lead to a more efficient and accurate web page classification results than existing approaches.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .L5. Source: Masters Abstracts International, Volume: 45-01, page: 0359. Thesis (M.Sc.)--University of Windsor (Canada), 2006