Notes On The 's On Multimedia Mining

computer helped Multimedia to achieve its higher performance. Multimedia concepts are used in different application. The

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

4) Technically speaking, data mining is a process that uses statistical, mathematical, and artificial intelligence techniques to extract and identify useful information and

In its infancy, data mining was as limited as the hardware being used. Large amounts of data were difficult to analyze because the hardware simply could not handle it [1]. The term "data mining" first began appearing in the 1980 's largely within the research and computer science communities. In the 1990 's it was considered a subset of a process called Knowledge Discovery in Databases of KKD [1]. KKD analyzes data in the search for patterns that may not normally be recognized with the naked eye. Today however, data mining does not limit itself to databases,

Lot of researches and studies are being conducted in analyzing this data and “Data Analytics” has become a subject by itself. Data mining processes great potential, as the data has predictive power; the

Abstract— Data mining, also popularly known as Knowledge Discovery in Database, refers to extracting or “mining" knowledge from large amounts of data. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Many application areas such as medical, research, stock market, weather forecasting, business strategies…etc data mining is very much helpful to gain the hidden and useful information. Nowadays the universities also have been started to use the data mining in-order to achieve highest quality in teaching. One way to achieve highest level of quality in higher education system is by discovering knowledge for prediction regarding enrolment of students in

The advances in the data capturing, storage, and communication technologies have made vast amounts of video data available to consumer and enterprise applications [1]. However, interacting with multimedia data, and video in particular, requires more than connecting with data banks and delivering data via networks to customers, homes or offices. We still have limited tools and applications to describe, organize, and manage video data. The fundamental approach is to index video data and make it a structured media. Manually generating video content description is time consuming and thus more costly to the point that it’s almost impossible. This is because of the structure of video data,

In the year of 2001 when the use of data mining in marketing was a relatively new concept Shaw ,Subramaniam, Tan and Welge gave an insight about management of large database using data mining techniques. They brought the concept of identifying useful information from the large customer database by identifying hidden patterns. They integrated data mining and marketing knowledge management to help in managing marketing decisions.

Background - One of the most promising developments in the field of computing and computer memory over the past few decades has been the ability to bring tremendous complex and large data sets into database management that are both affordable and workable for many organizations. Improvement in computer power has also allowed for the field of artificial intelligence to evolve which also improves the sifting of massive amounts of information for appropriate use in business, military, governmental, and academic venues. Essentially, data mining is taking as much information as possible for a variety of databases, sifting it intelligently and coming up with usable information that will help with data prediction, customer service, what if scenarios, and extrapolating trends for population groups (Ye, 2003; Therling, 2009).

As computer and database technologies develop rapidly, data accumulates in a speed unmatchable by human capacity of data processing[2]. Data mining as a multidisciplinary joint effort from databases, machine learning and statistics, is championing in turning mountains of data into nuggets. Researchers and practitioners realize that in order to use data mining tools effectively, data processing is essential to successful data mining.PrimitiveThese are features which have an influence on the output and their role cannot be assumed by the rest.[1]

The purpose of this chapter is to show an in depth review of the topics, areas and works related to the research presented here. we conduct a brief but comprehensive in depth review of data mining and association rule mining approaches and techniques, followed by a focus at interestingness & quality, and redundancy issues related to association rule mining. This review sets the basic work for our research and the proposals made here.

Data mining, an interdisciplinary subfield of computer science, is the computational methodology of finding examples in substantial data sets including routines at the crossing point of manmade brainpower, machine learning,

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

The Data mining it also be known as that the way of picking the data and from big mix of Information from the cloud. And it can also be say’s like it’s a data mining is digging or extracting knowledge from the data.

A wide range of possible applications for CBIR technology has been identified (Gudivada and Raghavan, 1995). This section presents