Multimedia Data mining (cover all aspect of data mining)

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Multimedia data mining it a cover all main aspect of data mining

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PRESENTED BY : TANYA AGRAWAL IT 3 rd .yr

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CONTENT: Brief Introduction about Data Mining. Multimedia Data Mining Text Mining. Image Mining. Audio Mining. Video Mining . USES. CONCLUSION

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INTRODUCTION OF DATA MINING FINIDING HIDDEN INFORMATION IN THE DATABASE. T he process of extracting unknown knowledge, and detecting interesting patterns from a massive set of data , involving methods at the intersection of artificial intelligence , machine learning .. e.t.c

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MULTIMEDIA DATAMINING(MDM) DEFINE AS : Process of finding intersecting patterns from media data such as audio, video, image ,text that are not ordinary accessible by basic query and associated result. USE: -Improve Decision Making. MDM as tools: Organize, Manage, Search. Surveillance , meeting, Broadcast, News, Sports, Archives, Movies, Medical … e.t. c

What Is Text Mining?: 

It useful for tasks such as: • Routing email to the appropriate department. • Analyzing open-ended survey question What Is Text Mining ? “ The objective of Text Mining is to explore information contained in textual documents in various ways, including …discovery of patterns and trends in data, associations among entities, predictive rules, etc.” (Grobelnik et al., 2001)

Text Mining: 

Text Mining Finding Patterns Finding “Nuggets” Novel Non-Novel Non-textual data General data-mining Exploratory Data Analysis Database queries Textual data Computational Linguistics Information Retrieval

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Natural Language Processing Semantic Web Web2.0 Information Retrieval Text Analytics Machine Learning Text Mining Data Analysis Computational Linguistics Search & DB Knowledge Rep. & Reasoning / Tagging

Text Mining Tasks: 

Text Mining Tasks Exploratory Data Analysis Information Extraction Text Classification

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PREPROCESSING Text document is split into the stream of words by removing all punctuation mark & by replacing tabs(Tokenization Process). The set of different words obtained by merging all text documents of a collection is called the dictionary of a document collection. Classification : Bayesian approaches , Decision trees & KNN based methods are standard tool in data mining .

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CLUSTERING: The Bi-Section-k means algorithm is able to deal with the large size of textual data. Bi-Section-K-means algorithm is a fast & high-quality clustering algorithm for text document. VISUALIZATION : Various text mining & data visualization tools have been described in the literature . Some tool are Clear Forest ,Invention Machine ,Goldfire Innovator..etc

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IMAGE MINING: In general, image relating it to the (human) visual perception and understanding. . A picture is characterized by its primitive features such as color, texture, shape at different scales. To reduce complexity, to capture the class structure, and discover causalities and to provide computational advantages, the models are likely to be analyzed hierarchically .

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Audio mining . Audio information retrieval is the process of retrieving audio information from the available resources It is processed by a speech recognition system in order to identify word or phoneme units

What are some Audio IR Mechanisms?: 

What are some Audio IR Mechanisms? Text based Peer-to-peer file sharing software FTP Streaming audio Websites Online network drives Clip Art Audio search devices and software

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Audio Search: Content-Based Retrieval New device by Philips Electronics in the Netherlands (hope to hit consumer market 2004) Microphone device captures your voice “Audio fingerprints” are determined Melody query then is sent to a database Results are returned Good for finding a song you don’t know by name but know by tune

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Attributes of an audio signal used to index ->Amplitude ->Frequency Audio Search: Content-Based Retrieval ->Average energ y ->Bandwidth ->Brightness ->Pitch:

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Audio Search: Content-Based Retrieval Positives Quick and easy searching of databases Less problems with text labeling Negatives Sometimes there are difficulties with speech and sound recognition

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VIDEO MINING It is a process of automatically extract content and structure of the video . By using video mining technique we can video summarization, classification . .

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VIDEO SEGMENTATION

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MOTION FEATURE EXTRACTION : This technique to extract the motion from a segment, and represent it in a comparable form. We compute Total Motion Matrix (TMM ), Average Motion Matrix (AMM), and its corresponding Total Motion (TM ) and Average Motion (AM ). The TMM, AMM , TM & AM for this segment with n frames is computed by using following algorithm

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CLUSTERING : - The clustering approach organize similar video object by their feature into the clusters. -The cluster can be used to evaluate the video shots that contain those video objects. -Multi-level hierarchical clustering approach to group segments in terms of category, and motion of segments.

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. EXPERIMENTAL RESULTS:- How does the proposed segmentation algorithm work to group incoming frames? How do TM,AM and the proposed algorithm work for clustering of segments?

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Performance of Video Segmentation

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CONCLUDING REMARKS

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APPLICATION: CONQUEST [1] system :-

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The MultiMedia Miner [2] :

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BROADCAST NEWS[3 ] :

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Echocardiogram[4] :

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OVER ALL CONCLUSION MULTIMEDIA DATA MINING IS A EFFECTIVE WAY TO EXTRACT THE INFORMATION EVEN IF DATA IS IN DIFFERENT FORM.