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Spatial Data Mining

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Spatial Data Mining

Spatial Data Mining

• What Does Spatial Data Mining Mean?
• Spatial data mining is the application of data mining to spatial models.
• In spatial data mining, analysts use geographical or spatial information to produce business intelligence or other results.
• This requires specific techniques and resources to get the geographical data into relevant and useful formats
• Challenges involved in spatial data mining include identifying patterns or finding objects that are relevant to the questions that drive the research project.
• Analysts may be looking in a large database field or other extremely large data set in order to find just the relevant data, using GIS/GPS tools or similar systems.

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• One interesting thing about the term "spatial data mining" is that it is generally used to talk about finding useful and non-trivial patterns in data.
• One way analysts may do this is by combing through data looking for "same-object" or "object-equivalent" models to provide accurate comparisons of different geographic locations.

• Spatial Data mining
• Multimedia Data mining
• Text Mining
• Mining the World Wide Web

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Spatial Data Mining

• What is Multimedia Data Mining?
• Multimedia mining is a subfield of data mining that is used to find interesting information of implicit knowledge from multimedia databases.
• Mining in multimedia is referred to as automatic mining.
• Mining multimedia data requires two or more data types, such as text and video or text video and audio.
• Multimedia data mining is an interdisciplinary field that integrates image processing and understanding, computer vision, data mining, and pattern recognition.
• Multimedia data mining discovers interesting patterns from multimedia databases that store and manage large collections of multimedia objects.

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• Challenges with Multimedia Database
• There are still many challenges to multimedia databases, such as:
• Modelling: Working in this area can improve database versus information retrieval techniques
• Design: The conceptual, logical and physical design of multimedia databases.
• Storage: Storage of multimedia database on any standard disk presents
• Performance: Physical limitations dominate an application involving video playback or audio-video synchronization
• Queries and retrieval: For multimedia data like images, video, and audio accessing data through query open up many issues like efficient query formulation, query execution and optimization, which need to be worked upon

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Spatial Data Mining

• Where is Multimedia Database Applied?
• Below are the following areas where a multimedia database is applied, such as:
• Documents and record management: Industries and businesses keep detailed records and various documents. For example, insurance claim records.
• Knowledge dissemination: Multimedia database is a very effective tool for knowledge dissemination in terms of providing several resources. For example, electronic books.
• Education and training: Computer-aided learning materials can be designed using multimedia sources which are nowadays very popular sources of learning. Example: Digital libraries.

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• Travelling: Marketing, advertising, retailing, entertainment and travel. For example, a virtual tour of cities.
• Real-time control and monitoring: With active database technology, multimedia presentation of information can effectively monitor and control complex tasks. For example, manufacturing operation control.

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• Categories of Multimedia Data Mining
• Multimedia mining refers to analyzing a large amount of multimedia information to extract patterns based on their statistical relationships.
• Multimedia data mining is classified into two broad categories: static and dynamic media.
• Static media contains text (digital library, creating SMS and MMS) and images (photos and medical images).
• Dynamic media contains Audio (music and MP3 sounds) and Video (movies).

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Spatial Data Mining

• Application of Multimedia Mining

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• Process of Multimedia Data Mining

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Spatial Data Mining

• Converting Un-structured data to structured data:

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• Architecture for Multimedia Data Mining
• Multimedia mining architecture is given in the below image. The architecture has several components. Important components are Input, Multimedia Content, Spatioltemporal Segmentation, Feature Extraction, Finding similar Patterns, and Evaluation of Results.

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• Models for Multimedia Mining
• The models which are used to perform multimedia data are very important in mining. Commonly four different multimedia mining models have been used. These are classification, association rule, clustering and statistical modelling.

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World Wide Web Mining

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Web Mining

• Over the last few years, the World Wide Web has become a significant source of information and simultaneously a popular platform for business.
• Web mining can define as the method of utilizing data mining techniques and algorithms to extract useful information directly from the web, such as Web documents and services, hyperlinks, Web content, and server logs.
• The World Wide Web contains a large amount of data that provides a rich source to data mining.
• The objective of Web mining is to look for patterns in Web data by collecting and examining data in order to gain insights.

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Web Mining

• Web Mining is the process of Data Mining techniques to automatically discover and extract information from Web documents and services.
• The main purpose of web mining is discovering useful information from the World-Wide Web and its usage patterns.

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Web Mining

• Web Content Mining: Web content mining is the application of extracting useful information from the content of the web documents.
• Web content consist of several types of data – text, image, audio, video etc. Content data is the group of facts that a web page is designed.
• It can provide effective and interesting patterns about user needs.
• Text documents are related to text mining, machine learning and natural language processing. This mining is also known as text mining.
• This type of mining performs scanning and mining of the text, images and groups of web pages according to the content of the input.
• The primary task of content mining is data extraction, where structured data is extracted from unstructured websites.

For Example, if any user searches for a specific task on the search engine, then the user will get a list of suggestions.

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• Web Structure Mining: Web structure mining is the application of discovering structure information from the web.
• The structure of the web graph consists of web pages as nodes, and hyperlinks as edges connecting related pages.
• Structure mining basically shows the structured summary of a particular website.
• It identifies relationship between web pages linked by information or direct link connection.
• To determine the connection between two commercial websites, Web structure mining can be very useful.

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• Web Usage Mining: Web usage mining is the application of identifying or discovering interesting usage patterns from large data sets.
• Web usage mining is used to extract useful data, information, knowledge from the weblog records, and assists in recognizing the user access patterns for web pages
• And these patterns enable you to understand the user behaviors or something like that.
• In web usage mining, user access data on the web and collect data in form of logs.
• So, Web usage mining is also called log mining.

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• Session and visitor analysis:
• The analysis of preprocessed data can be accomplished in session analysis, which incorporates the guest records, days, time, sessions, etc. This data can be utilized to analyze the visitor's behavior.
• The document is created after this analysis, which contains the details of repeatedly visited web pages, common entry, and exit.
• OLAP (Online Analytical Processing):
• OLAP accomplishes a multidimensional analysis of advanced data.
• OLAP can be accomplished on various parts of log related data in a specific period.
• OLAP tools can be used to infer important business intelligence metrics

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• Challenges in Web Mining:
• The complexity of web pages:
• The site pages don't have a unifying structure.
• They are extremely complicated as compared to traditional text documents
• The web is a dynamic data source:
• The data on the internet is quickly updated. For example, news, climate, shopping, financial news, sports, and so on.
• Diversity of client networks:
• There are over a hundred million workstations that are associated with the internet and still increasing tremendously.
• The web is too broad:
• The size of the web is tremendous and rapidly increasing.
• It appears that the web is too huge for data warehousing and data mining.

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Application of Web Mining:

• Marketing and conversion tool
• Data analysis on website and application accomplishment.
• Audience behavior analysis
• Advertising and campaign accomplishment analysis.
• Testing and analysis of a site.

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