ISSN: 2277-9655 [Goudannavar* et al., 7(3): March, 2018] Impact Factor: 5.164 IC™ Value: 3.00 CODEN: IJESS7 http: // www.ijesrt.com© International Journal of Engineering Sciences & Research Technology [449] IJESRT INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY REVIEW ON WEB MULTIMEDIA MINING AND KNOWLEDGE DISCOVERY Suraj Jain 1 , Siddu P. Algur 2 , *Basavaraj A. Goudannavar 3 , Prashant Bhat 4 1,2,3 Department of Computer Science, Rani Channamma University, Belagavi-591156, Karnataka, India 4 Department of Business Analytics/Data Science, Chris Institute of Management, Lavasa-412112, Pune, Maharashtra, India DOI: 10.5281/zenodo.1199346 ABSTRACT Web Multimedia data mining (WMDM) can be defined as the process of finding interesting patterns from media data such as audio, video, image and text that are not ordinarily accessible by basic queries and associated results. MDM is the mining of knowledge and high level multimedia information from large multimedia database system. MDM refers to pattern discovery, rule extraction and knowledge acquisition from multimedia database. To extract knowledge from multimedia database multimedia techniques are used. We compare MDM techniques with the state of the art data mining techniques involving clustering, classification, sequence pattern mining, association rule mining and visualization. This paper is a review on Web multimedia mining (WMM) and Knowledge discovery it elaborates basic concepts, application at various areas, techniques, approaches and other useful areas which need to be work for WMM. Analyzing this huge amount of multimedia data to discover useful knowledge is a challenging problem which has opened the opportunity for research in WMM and knowledge discovery. KEYWORDS: Multimedia Mining, Feature Extraction, Knowledge discovery I. INTRODUCTION The recent abundance of digital information available electronically has made the organization of texture information into an important task. Web multimedia content mining is burgeoning new technology for discovering knowledge from web multimedia based on complementary resources. 1.1 Web Multimedia Mining Knowledge Discovery from Data (KDD) was introduced at the beginning of nineties. Taking into account the polymorphism of multimedia data, multimedia data mining was recently proposed as a new topic of research [1]. The World Wide Web serves as huge, widely distributed global information service centre for news, advertisements, consumer information, financial management, education, government, e-commerce and many other information services. The size of web is in order of hundreds of terabytes and still growing rapidly. The properties of web: the huge, diverse, and dynamic and thus raises the scalability, multimedia data and temporal issues respectively. At the extreme knowledge discovery, i.e., identification of novel information through inference mechanisms consider as web mining [2]. Historically, the conception of discovering useful patterns in data has been given a variety of names like data mining, knowledge extraction, Information discovery, Information harvesting, data archaeology, and data pattern processing. The Web Mining is about to discovery of knowledge in all its forms, everywhere on the web. It is the process of discovering potentially useful and previously unknown information or knowledge from the web data. Three distinct categories based on [3], are: the application of data mining techniques to extract and prepare knowledge from Web content (include text, image and video), structure (hyperlinks between documents), and usage (logs of web sites). Web content mining describes the discovery of useful information from web contents data/documents. The web content consists of several types of data such as textual, image, audio, metadata and hyper links. The web multimedia contents analysis has been facing lots of research challenges due to the multimedia mining and knowledge discovery deals with non structured information.