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Author: Admin | 2025-04-28
Related papersWeb Video Mining: Metadata Predictive Analysis using Classification TechniquesNow a days, the Data Engineering becoming emerging trend to discover knowledge from web audio-visual data such as- YouTube videos, Yahoo Screen, Face Book videos etc. Different categories of web video are being shared on such social websites and are being used by the billions of users all over the world. The uploaded web videos will have different kind of metadata as attribute information of the video data. The metadata attributes defines the contents and features/characteristics of the web videos conceptually. Hence, accomplishing web video mining by extracting features of web videos in terms of metadata is a challenging task. In this work, effective attempts are made to classify and predict the metadata features of web videos such as length of the web videos, number of comments of the web videos, ratings information and view counts of the web videos using data mining algorithms such as Decision tree J48 and navie Bayesian algorithms as a part of web video mining. The results of Decision tree J48 and navie Bayesian classification models are analyzed and compared as a step in the process of knowledge discovery from web videos.Metadata Based Classification and Analysis of Large Scale Web VideosThe astonishing growth of videos on the Internet such as YouTube, Yahoo Screen, Face Book etc, organizing videos into categories is of paramount importance for improving user experience and website utilization. In this information age, video information is the rapidly sharing by the people through social media websites such as YouTube, Face Book, yahoo Screen etc. Different categories of web video are shared on social websites and used by the billions of users all over the world. The classification/partitioning of web videos in terms of length of the video, ratings, age of the video, number of comments etc, and analysis of this web video as a unstructured complex data is a challenging task. In this work we propose effective classification model to classify each category of web-videos (Ex- ‘Entertainment’, ‘People and Blogs’, ‘Sports’, ‘News and Politics’, ‘Science and Technology’ etc) based on other web metadata attributes as splitting criteria. An attempt is made to extract metadata from web videos. Based on the extracted metadata, web videos are classified/partitioned into different categories by applying data mining classification algorithms such as and Random Tree and J48 classification model. The classification results are compared and analyzed using cost/benefit analysis. Also the results demonstrate classification of web videos depends largely on available metadata and accuracy of the classification model. Classification/partitioning of web-based videos are important task with many applications in video search and information retrieval process. However, collecting metadata required for classification model may be prohibitively expensive. The experimental difficulties arise from large data diversity within a category is pitiable of metadata and dreadful conditions of web video metadata.Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata ObjectsNowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is
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