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Data Mining On Multimedia Data
Post: #1

Data Mining On Multimedia Data

Introduction:

Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyzes offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations.

Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?"

Existing System:

Due to the bulk of information that is present n the system it is not easy to find the desired data. Now this can cause a large loss of time and resources and economy.

Proposed System:


Here in the proposed system we are using the help of a search engine with advanced searching options. By using this way we can collect the desired information from the web with the least effort and time consumption. Web mining aims to discover useful information and knowledge from the Web hyperlink structure, page contents, and usage data. Although Web mining uses many conventional data mining techniques, it is not purely an application of traditional data mining due to the semi structured and unstructured nature of the Web data and its heterogeneity. It has also developed many of its own algorithms and techniques.

The data can be of many types generally images, videos etc depending on the persons taste. The images will be searched using CBIR(Content Based Image Retrieval) and thumbnails will be shown for the images which on further selection will be displayed in the true form. For the videos the searching of the videos can be based on size or the name of the respective video.

The architecture for the proposed system will be having a number of layers. The first layer is application that the user will be using. The second layer will be the API that calls the method for performing the specific data searching and the third layer will be the engine that performs the searching of the data. The engine will respond to the server according to the API.

Advantages:

Data mining can assist financial institutions in areas such as credit reporting and loan information.

Data mining can aid law enforcers in identifying criminal suspects as well as apprehending these criminals by examining trends in location, crime type, habit, and other patterns of behaviors.

Data mining can assist researchers by speeding up their data analyzing process; thus, allowing them more time to work on other projects.

SOFTWARE SPECIFICATION

Front end : Java
Back end : My SQL
Operating system : LINUX/ Windows
IDE : Net Beans


HARDWARE SPECIFICATION


Processor : Pentium IV OR Above
Primary Memory : 256 MB RAM
Storage : 40 GB Hard Disk
Display : VGA Color Monitor

.doc  Data Mining On Multimedia Data.doc (Size: 191 KB / Downloads: 116)
Post: #2
I need full report of this Data mining on multimedia data and also source code in java
please I need help
Post: #3
hey firend I need help on this project
Post: #4
there is an attached file in seminarsonly's post. please download it. it may contain full details.
Post: #5
Multimedia Databases and Data Mining



.ppt  SVD-II.ppt (Size: 396 KB / Downloads: 51)

Indexing - Detailed outline

primary key indexing
secondary key / multi-key indexing
spatial access methods
fractals
text
Singular Value Decomposition (SVD)
multimedia

SVD - Other properties - summary

can produce orthogonal basis (obvious) (who cares?)
can solve over- and under-determined linear problems (see C(1) property)
can compute ‘fixed points’ (= ‘steady state prob. in Markov chains’) (see C(4) property)

Kleinberg’s algorithm

on the resulting graph, give high score (= ‘authorities’) to nodes that many important nodes point to
give high importance score (‘hubs’) to nodes that point to good ‘authorities’)

Query feedbacks

[Chen & Roussopoulos, sigmod 94]
sample problem:
estimate selectivities (e.g., ‘how many movies were made between 1940 and 1945?’
for query optimization,
LEARNING from the query results so far!!
 

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