UNIT-IV
Graph Mining, Social Network analysis and multi- relational data mining, Spatial data mining, Multimedia data mining, Text mining, Mining the world wide web(www), Data mining applications, Social impacts of data mining, Trends in data Mining.
Multi-Relational Data Mining (MRDM)
Multi-relational data mining (MRDM) is a form of
Data Mining operating on data stored in multiple database tables.
MRDM is a multi-disciplinary field which deals with
the Knowledge discovery from relational database which consist of number of relations.
MRDM is required in domains where the data are highly structured.
The multi relational data mining approach has
developed as an alternative way for handling the structured data such that RDBMS. It provides mining in multiple tables directly.
Three popular pattern finding techniques
classification, clustering and association are frequently used in MRDM.
Multi Relational Data Mining algorithms look for patterns among multiple tables (relational patterns).
MRDM APPROACHES
There are so many approaches supported by the Multi Relational Data Mining these are as below:
Inductive Logic Programming (ILP): This ILP paradigm says that how the logic program will convert the patterns.
Multi-relational Clustering: This technology is used to
cluster the tuples in the target table in the relational database, so calculating the distance of relations in the target table is the main task in the multi-relation clustering.
Probabilistic Relational Models: A probabilistic
relational model (PRM) or a relational probability model is a model in which the probabilities are specified on the relations, independently of the actual individuals.
Different individuals share the probability parameters.
Multi-relational Data Mining framework is based on
the Search for interesting patterns in the relational database,
It is a framework which deals with gathering the data
about the data (metadata) from a database and choose the best approach to get the optimal results.
Multi-relational Data Mining framework
Figure: MRDM Framework Architecture
MRDM Framework Architecture
Data understanding means gathering the metadata
from the database which describes the best approach of the analysis.
Data Preparation means transformation of the database into MRDM formats where we select the algorithms.
Spatial Data Mining
Spatial data mining is the process of discovering interesting,
useful, non-trivial patterns from large spatial datasets – E.g.
Determining hotspots: unusual locations.
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.
Spatial Data Mining Tasks
Characteristics rule. – A spatial characteristic rule is a general
description of spatial data. For example, a rule describing the general price range of houses in various geographic regions in a city is a spatial characteristic rule.
Discriminate rule. E.g. Comparison of price ranges of different geographical area.
Association rule-: we can associate the non spatial attribute to spatial attribute or spatial attribute to spatial attribute.
Clustering rule-: helpful to find outlier detection which is useful to find suspicious knowledge E.g. Group crime location.
Classification rule-: it defines whether a spatial entity
belong to a particular class or how many classes will be classified. e.g. Remote sensed image based on spectrum and GIS data.
Trend detection-A trend is a temporal pattern in some time
series data. Spatial trend is defined as consider a non spatial attribute which is the neighbour of a spatial data object.
Spatial trends describe a regular change of non- spatial attributes when moving away from certain start objects. e.g. economic power, is an important issue in economic geography.
Mining the world wide web(www)
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.
There are three types of data mining:
1. Web Content Mining:
Web content mining can be used to extract useful data, information, knowledge from the web page content.
In web content mining, each web page is considered as an individual document.
The primary task of content mining is data extraction,
where structured data is extracted from unstructured websites.
Example, if any user searches for a specific task on the search engine, then the user will get a list of suggestions.
2. Web Structured Mining:
The web structure mining can be used to find the link structure of hyperlink.
It is used to identify that data either link the web pages or direct link network.
In Web Structure Mining, an individual considers the
web as a directed graph, with the web pages being the vertices that are associated with hyperlinks.
Structure and content mining methodologies are usually combined.
3. Web Usage Mining:
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.
In Mining, the usage of web resources, the individual is thinking about records of requests of visitors of a website, that are often collected as web server logs.
While the content and structure of the collection of
web pages follow the intentions of the authors of the pages, the individual requests demonstrate how the consumers see these pages.
Web usage mining may disclose relationships that were not proposed by the creator of the pages.
Application of Web Mining:
Web mining has an extensive application because of
various uses of the web. The list of some applications of web mining is given below.
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.
Challenges in Web Mining:
The complexity of web pages
The web is a dynamic data source
Diversity of client networks:
Relevancy of data:
The web is too broad:
Trends in Data Mining
Data mining concepts are still evolving and here are the latest trends that we get to see in this field −
Application Exploration.
Scalable and interactive data mining methods.
Integration of data mining with database systems, data warehouse systems and web database systems.
Standardization of data mining query language.
Visual data mining.
New methods for mining complex types of data.
Biological data mining.
Data mining and software engineering.
Web mining.
Distributed data mining.
Real time data mining.
Multi database data mining.
Privacy protection and information security in data mining.
