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.