The Issues On Multimedia Mining

Data mining has popular technology for extracting interesting information for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of several types of data set. Multimedia data are unstructured data or semi-structured data. These data are stored in multimedia database, multimedia mining which is used to find information from large multimedia database system, using multimedia techniques and powerful tools. The current approaches and techniques are explained for mining multimedia data. This paper analyzes about the use of essential characteristics of multimedia data mining, retrieving information is one of the goals of data mining and different issues have been discussed.

Data Mining. It is the process of discovering interesting knowledge that are gathered and significant structures from large amounts of data stored in data warehouse or other information storage.

Data mining is a class of database applications that looks for hidden patterns in a group of data that can be

Data mining is the procedure of identifying new hidden, insights and unexpected patterns in large data. Analyzing and exploring the huge volume of data sets and information becomes increasingly very difficult. Visualization of data mining helps to deal with a flood of data and information. As a volume of a large set of data stored and collected in databases develops, there is a developing need to provide a data summarization through visualization. The benefit of visual information investigation is that the client directly involves in the process of

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

In a world where computers are becoming as essential to daily life as the cars we drive or the telephones we use to communicate, it is difficult to find a person who doesn’t have some particular use for computers. Computers have become the information stores of the world. If you take a moment to think about all the kinds of information a person can and does hold on their computer it is staggering. I myself have all the passwords to my email and bank accounts, the history of every web page I’ve visited in the last 3 weeks, my credit card numbers, the complete history of all my banking transactions for the last three years stored on my computer. Additionally, think about all the

Data mining is defined as the process of exploration and analysis of large data sets, and discovering meaningful patterns and rules. The main objective of data mining is to design and work efficiently with large data sets. Data mining helps resolving problems that are time consuming when traditional techniques are used. Data mining techniques are used to predict future trends and to make wise decisions. There are multiple Data Mining techniques available to the Data diggers to make their life easy. In my study report I will be discussing about the different mining techniques, advantages and disadvantages and also about a use case of the data mining techniques on shark attack dataset to predict the attack of sharks based on various attributes.

Data mining is the extraction of knowledge from the various databases that was previously unknown (Musan & Hunyadi, 2010). Data mining consists of using software that conglomerates artificial intelligence, statistical analysis, and systems management in the act of extracting facts and understanding from data stored in data warehouses, data marts, and through metadata (Giudici, 2005). Through algorithms and learning capabilities data mining software can analyze large amounts of data and give the management team intellectual and effective information to help them form their decisions. The intention for data mining is to analyze prevailing data and form new truths and new associations that were unknown prior to the analysis (Musan & Hunyadi,

Multimedia data mining refers to the analysis of large amounts of multimedia information in order to find patterns or statistical relationships. Once data is collected, computer programs are used to analyze it and look for meaningful connections. This information is often used by governments to improve social systems. It can also be used in marketing to discover consumer habits.

Huge amounts of data are collected nowadays from different application domains and are not feasible to analyze all these data manually. Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In recent years, soft computing became more and more attractive for the researchers, who work in the related research field of data mining. This paper concerns primarily about how to use soft computing model to extract knowledge from data mining (database). The data mining preprocessing techniques have been applied on the available data to clean it in proper form to extract the knowledge from the data. Thereafter, statistical analysis and soft computing techniques have been applied on the clean data to select the preferable model. The decision of the preferable model has to be achieved based on the maximum number of minimum value of the parameters of residual analysis and average error. The preferable model has been used to extract the knowledge from the data mining. The goal of the

Data mining is a popular technology for extracting interesting information for multimedia data sets, such as audio, video, images, graphics, speech, text and combination of several types of data set. Multimedia data are unstructured data or semi-structured data. These data are stored in multimedia database, multimedia mining which is used to find information from large multimedia database system, using multimedia techniques and powerful tools. This paper analyzes about the use of essential characteristics of multimedia data mining, retrieving information is one of the goals of data mining and different issues have been discussed. The current approaches and techniques are explained for mining multimedia data. In this paper applications and models of multimedia mining are discussed clearly. Nowadays Multimedia mining has become popular field in research area.

Multimedia concept detection is a challenging topic due to the well known class imbalance issue especially in a big data environment. With the rapid growth of multimedia data such as audio, image and video, as well as text data, applying powerful data mining approaches is a necessity to tackle issues of large and imbalanced datasets. For this purpose, in this paper, an Importance Factor based Multiple Correspondence Analysis (IF-MCA) framework is proposed with MapReduce implementation for dealing with large scale dataset. Specifically, a Hierarchical Information Gain Analysis method inspired by decision tree algorithm is combined with the Affinity Propagation (AP) algorithm for critical feature selection and Importance Factor (IF) assignment according to the ordering of

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

UnStructured Data is any data with unknown form or structure like audio files, video files and images etc. Processing of this unstructured data is difficult.

Data mining is an area of data processing in which extraction of useful patterns from pre-existing databases and transformation of extracted information into understandable form is done. Data mining employs various methods like clustering, classification, regression etc.[1] One such method is Association Rule Mining which discovers the dependencies between database variables. Frequent Pattern Mining is an area of data mining which works on this principle and generates