Multimedia Data And Its Essential Characteristics

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

Usually the data mining analysis is done by grouping commonly co-occuring things (Associations), discovering time-ordered events (Sequences), anticipating future occurences (Predictions), identifying natural groupings of items (Clusters) and finally, by uncovering generalizations to help classify items (Classification). These different type of mining usually take a lot of time and a good understanding of the business and

Abstract - In the Data mining process, we can identify the patterns in the data that is hard to find using normal analysis. Several Mathematical and statistical algorithms are used in this approach to determine the probability of the event or scenario. The main aim of this process

Baker, 2009) (wiley, 2015) Text mining is generally identical to content examination it is the way toward getting brilliant data from text. Fantastic data is ordinarily determined through the concocting of examples and patterns through means, for example, factual example learning. Content mining as a rule includes the way toward organizing the information message typically parsing, alongside the expansion of some determined phonetic highlights and the expulsion of others, and ensuing inclusion into a database, inferring designs inside the organized information, lastly assessment and elucidation of the yield. 'High caliber' in content mining more often than not alludes to some blend of pertinence, oddity, and interesting .Text examination programming can help by transposing words and expressions in unstructured information into numerical esteems which would then be able to be connected with organized information in a database and broke down with conventional information mining techniques. Text mining is a minor departure from a field called information mining that tries to discover

Data mining has been come increasingly easier in recent years. It cannot be done manually because it requires applying mathematics, statistics, and pattern matching to large amounts of data[iv] but advances in computer hardware and software have made data mining on a large scale a reality. This has

2. (10 pts) How is text mining different than data mining? Text mining is a process which collects information and knowledge from large amounts of unstructured data sources. When I say unstructured data sources, I am talking about Pdf files, Word documents, XML files, text excerpts etc… Text mining collects information from text. Text mining is different than data mining because data mining is a process which collects information and knowledge from large amounts of structured data sources. Structured data sources means that data are classify by categorical, ordinal, or continuous variables, and the goal of data mining is to transform data into model or understandable structure after collecting information from data. However they are

Text mining sometimes known as text data mining often refers to the process of pulling out of interesting and non-trivial patterns of knowledge form a semi or unstructured text document. Text mining can also serve as an extension of data mining or of data finding from a structures database. With text mining it can be the same as data mining but with a bit more complexity, because they somewhat carry out the same processes and has the same purpose, however with text mining the data is more unstructured rather that structured in the data files such as : (pdf, word, xml etc.). This is so because most people store information in the form of text, it is believed that text mining can be greater than data mining, during recent years there where a number of studies done which indicates that 80% of business information is stored in text format.

Data mining is used in variety of fields and applications (Galit, Stiumueli, Natin & Peter 2010). This includes the military for purposes of intelligence,

Introduction Data, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. I am always fascinated to know how this large amount of data is handled, stored in databases and manipulated to extract useful information. A raw data is like an unpolished diamond, its value is known only after it is polished. Similarly, the value of data is understood only after a proper meaning is brought out of it, this is known as Data Mining.

Index Terms—Face detection, face annotation, huge To extract images from database by CBIR method, firstly user has to provide retrieval system and query image or sample facial image, after that retrieval system will perform operation on query image and change it into internal representation of feature vector. Then similar feature of feature between feature vectors of query image in database are calculated and retrieve the results in indexing form. The indexing results will help to find the image in dataset. This how the CBIR is working [14],[19] and also to more on study we used paper [18], which consist the study of 200 papers of CBIR method and parallel there was one more method is semantic image annotation, which overcome problems related with the CBIR method

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

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

Data mining: Current issues and challenges Abstract:- Data mining has pulled in a lot of interest in the data business and in the public eye overall lately, because of wide accessibility to massive amount of data and the up and coming need of turning such data into valuable data and information.

1. Introduction Globally, people are increasingly accessing content as easier access to information continues to explode rapidly. People not only access content (be it text, audio, still images, animation, video or interactivity content forms) but are themselves the producers of more and more digital data and with this comes a host of problems like content management, content reuse based on consumer and device capabilities, protection of rights and from unauthorised access or modification, privacy protection of both providers and consumers, etc [1] [2].

These necessities have prompted the conception of Data Mining that has been changing the live from the data age toward the coming information age. A considerable amount of literature has been published on Data Mining and the aim of this survey is concerned with the ideas behind the processes; purpose and techniques of Data Mining. [1][2]