Web data mining became an easy and important platform for retrieval of useful information. Users prefer World Wide Web more to upload and download data. As increasing growth of data over the internet, it is getting difficult and time consuming for discovering informative knowledge and patterns. Digging knowledgeable and user queried information from unstructured and inconsistent data over the web is not an easy task to perform. Different mining techniques are used to fetch relevant information from web (hyperlinks, contents, web usage logs). Web data mining is a sub discipline of data mining which mainly deals with web. Web data mining is divided into three different types: web structure, web content and web usage mining. All these types use different techniques, tools, approaches, algorithms for discover information from huge bulks of data over the web.

/pdf/data-mining-web-data-mining-techniques-tools-and-algorithms-3rl66tn8gk.pdf

Data Mining: Web Data Mining Techniques, Tools and Algorithms: An Overview

Emotions play a key role in human communication and public presentations. Human emotions are usually expressed through multiple modalities. Therefore, exploring multimodal emotions and their coherence is of great value for understanding emotional expressions in presentations and improving presentation skills. However, manually watching and studying presentation videos is often tedious and time-consuming. There is a lack of tool support to help conduct an efficient and in-depth multi-level analysis. Thus, in this paper, we introduce EmoCo , an interactive visual analytics system to facilitate efficient analysis of emotion coherence across facial, text, and audio modalities in presentation videos. Our visualization system features a channel coherence view and a sentence clustering view that together enable users to obtain a quick overview of emotion coherence and its temporal evolution. In addition, a detail view and word view enable detailed exploration and comparison from the sentence level and word level, respectively. We thoroughly evaluate the proposed system and visualization techniques through two usage scenarios based on TED Talk videos and interviews with two domain experts. The results demonstrate the effectiveness of our system in gaining insights into emotion coherence in presentations.

EmoCo : Visual Analysis of Emotion Coherence in Presentation Videos

This paper aims to identify the intellectual structure of four leading hospitality journals over 40 years by applying mixed-method approach, using both machine learning and traditional statistical analyses.,Abstracts from all 4,139 articles published in four top hospitality journals were analyzed using the structured topic modeling and inferential statistics. Topic correlation and community detection were applied to identify strengths of correlations and sub-groups of topics. Trend visualization and regression analysis were used to quantify the effects of the metadata (i.e. year of publication and journal) on topic proportions.,The authors found 50 topics and eight subgroups in the hospitality journals. Different evolutionary patterns in topic popularity were demonstrated, thereby providing the insights for popular research topics over time. The significant differences in topical proportions were found across the four leading hospitality journals, suggesting different foci in research topics in each journal.,Combining machine learning techniques with traditional statistics demonstrated potential for discovering valuable insights from big text data in hospitality and tourism research contexts. The findings of this study may serve as a guide to understand the trends in the research field as well as the progress of specific areas or subfields.,It is the first attempt to apply topic modeling to academic publications and explore the effects of article metadata with the hospitality literature.

Toward understanding the topical structure of hospitality literature: Applying machine learning and traditional statistics

Mining Multimedia Documents

Information on Internet and especially on Web sites increasing rapidly day by day, Web sites play an important role in this manner where a lot of Web users are always upload, download and brows a lot of contents based on their needs. Web server provide a way to browse these contents that scattered in many Web sites that reside in it by assigning an Internet Protocol (IP) address or Domain Name System (DNS) to be accessed around the world. Server log the requests that made by users to access their interesting content then store this information in the form of log file. Log file size can be growth from some kilobytes to several megabytes in few days depending on data traffic and the popularity of Web sites. With the fast growth of the data and information in Web environment made a necessity to use sophisticated techniques that have never used in other domains to extract knowledge and significant Web patterns. Web Mining is an extension of Data Mining that integrated various technology in research fields including Artificial Intelligence (AI), statistics, informatics, knowledge discovery and computational linguistics. The aim of Web Mining is to provide a algorithm or technique to make data accesses more efficient and convenient. Web Mining techniques are categorized into three classes depend on which part to be mined which are: Web Content Mining (WCM), Web Structure Mining (WSM) and Web Usage Mining (WSM), in this paper a survey of Web Mining techniques and application are discussed briefly.

A Survey on Web Mining Techniques and Applications

Multimedia data mining is a popular research domain which helps to extract interesting knowledge from multimedia data sets such as audio, video, images, graphics, speech, text and combination of several types of data sets. Normally, multimedia data are categorized into unstructured and semi-structured data. These data are stored in multimedia databases and multimedia mining is used to find useful information from large multimedia database system by using various multimedia techniques and powerful tools. This paper provides the basic concepts of multimedia mining and its essential characteristics. Multimedia mining architectures for structured and unstructured data, research issues in multimedia mining, data mining models used for multimedia mining and applications are also discussed in this paper. It helps the researchers to get the knowledge about how to do their research in the field of multimedia mining.

/pdf/multimedia-mining-research-an-overview-2s2sngrus4.pdf

A. Sakila

Papers

Multimedia mining research - an overview