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.
Multimedia data mining requires the collection of huge amounts of data. The sample size is important when analyzing data because predicted trends and patterns are more likely to be inaccurate with a smaller sample. This data can be collected from a number of different media, including videos, sound files, and images. Some experts also consider spatial data and text to be multimedia. Information from one or more of these media is the focus of data collection.
Whereas an analysis of numerical data can be straightforward, multimedia data analysis requires sophisticated computer programs which can turn it into useful numerical data. There are a number of computer programs available that make sense of the information gathered from multimedia data mining. These computer programs are used to search for relationships that may not be apparent or logically obvious.
When multimedia is mined for information, one of the most common uses for this information is to anticipate behavior patterns or trends. Information can be divided into classes as well, which allows different groups, such as men and women or Sundays and Mondays, to be analyzed separately. Data can be clustered, or grouped by logical relationship, which can help track consumer affinity for a certain brand over another, for example.
Multimedia data mining has a number of uses in today’s society. An example of this would be the use of traffic camera footage to analyze traffic flow. This information can be used when planning new streets, expanding existing streets, or diverting traffic. Government organizations and city planners can use the information to help traffic flow more smoothly and quickly.
While the term data mining is relatively new, the practice of mining data has been around for a long time. Grocery stores, for example, have long used data mining to track consumer behavior by collecting data from their registers. The numerical data relating to sales information can be used by a computer program to learn what people are buying and when they are likely to buy certain products. This information is often used to determine where to place certain products and when to put certain products on sale.