1. ODAM An Optimized Distributed Association
Rule Mining Algorithm
(Synopsis)

2. 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 analyses 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?"

3. Data mining (DM), also called Knowledge-Discovery in
Databases (KDD) or Knowledge-Discovery and Data Mining, is the
process of automatically searching large volumes of data for patterns
using tools such as classification, association rule mining, clustering,
etc.. Data mining is a complex topic and has links with multiple core
fields such as computer science and adds value to rich seminal
computational techniques from statistics, information retrieval,
machine learning and pattern recognition.
Data mining techniques are the result of a long process of research
and product development. This evolution began when business data
was first stored on computers, continued with improvements in data
access, and more recently, generated technologies that allow users to
navigate through their data in real time. Data mining takes this
evolutionary process beyond retrospective data access and navigation
to prospective and proactive information delivery. Data mining is ready
for application in the business community because it is supported by
three technologies that are now sufficiently mature:
o Massive data collection
o Powerful multiprocessor computers
o Data mining algorithms
Commercial databases are growing at unprecedented rates. A recent
META Group survey of data warehouse projects found that 19% of

4. respondents are beyond the 50 gigabyte level, while 59% expect to be
there by second quarter of 1996.1 In some industries, such as retail,
these numbers can be much larger. The accompanying need for
improved computational engines can now be met in a cost-effective
manner with parallel multiprocessor computer technology. Data mining
algorithms embody techniques that have existed for at least 10 years,
but have only recently been implemented as mature, reliable,
understandable tools that consistently outperform older statistical
methods.
With the explosive growth of information sources available on
the World Wide Web, it has become increasingly necessary for users to
utilize automated tools in find the desired information resources, and
to track and analyze their usage patterns. These factors give rise to
the necessity of creating serverside and clientside intelligent systems
that can effectively mine for knowledge. Web mining can be broadly
defined as the discovery and analysis of useful information from the
World Wide Web. This describes the automatic search of information
resources available online, i.e. Web content mining, and the
discovery of user access patterns from Web servers, i.e., Web usage
mining.

5. Web Mining is the extraction of interesting and potentially
useful patterns and implicit information from artifacts or activity
related to the WorldWide Web. There are roughly three knowledge
discovery domains that pertain to web mining: Web Content Mining,
Web Structure Mining, and Web Usage Mining. Web content mining is
the process of extracting knowledge from the content of documents or
their descriptions. Web document text mining, resource discovery
based on concepts indexing or agent based technology may also fall in
this category. Web structure mining is the process of inferring
knowledge from the World Wide Web organization and links between
references and referents in the Web. Finally, web usage mining, also
known as Web Log Mining, is the process of extracting interesting
patterns in web access logs.
Web Content Mining
Web content mining is an automatic process that goes beyond
keyword extraction. Since the content of a text document
presents no machinereadable semantic, some approaches have
suggested to restructure the document content in a
representation that could be exploited by machines. The usual
approach to exploit known structure in documents is to use
wrappers to map documents to some data model. Techniques
using lexicons for content interpretation are yet to come.

6. There are two groups of web content mining strategies: Those
that directly mine the content of documents and those that
improve on the content search of other tools like search engines.
Web Structure Mining
WorldWide Web can reveal more information than just the
information contained in documents. For example, links pointing
to a document indicate the popularity of the document, while
links coming out of a document indicate the richness or perhaps
the variety of topics covered in the document. This can be
compared to bibliographical citations. When a paper is cited
often, it ought to be important. The PageRank and CLEVER
methods take advantage of this information conveyed by the
links to find pertinent web pages. By means of counters, higher
levels cumulate the number of artifacts subsumed by the
concepts they hold. Counters of hyperlinks, in and out
documents, retrace the structure of the web artifacts
summarized.
Web Usage Mining
Web servers record and accumulate data about user interactions
whenever requests for resources are received. Analyzing the web
access logs of different web sites
can help understand the user behaviour and the web structure,

7. thereby improving the design of this colossal collection of resources.
There are two main tendencies in Web Usage Mining driven by the
applications of the discoveries: General Access Pattern Tracking and
Customized Usage Tracking.
The general access pattern tracking analyzes the web logs to
understand access patterns and trends. These analyses can shed light
on better structure and grouping of resource providers. Many web
analysis tools existd but they are limited and usually unsatisfactory.
We have designed a web log data mining tool, WebLogMiner, and
proposed techniques for using data mining and OnLine Analytical
Processing (OLAP) on treated and transformed web access files.
Applying data mining techniques on access logs unveils interesting
access patterns that can be used to restructure sites in a more
efficient grouping, pinpoint effective advertising locations, and target
specific users for specific selling ads.
Customized usage tracking analyzes individual trends. Its purpose is to
customize web sites to users. The information displayed, the depth of
the site structure and the format of the resources can all be
dynamically customized for each user over time based on their access
patterns.
While it is encouraging and exciting to see the various potential
applications of web log file analysis, it is important to know that the

8. success of such applications depends on what and how much valid and
reliable knowledge one can discover from the large raw log data.
Current web servers store limited information about the accesses.
Some scripts customtailored for some sites may store additional
information. However, for an effective web usage mining, an important
cleaning and data transformation step before analysis may be needed.

9. Abstract
With the explosive growth of information sources available on
the World Wide Web, it has become increasingly necessary for users to
utilize automated tools in find the desired information resources, and
to track and analyze their usage patterns.
Association rule mining is an active data mining research area.
However, most ARM algorithms cater to a centralized environment. In
contrast to previous ARM algorithms, ODAM is a distributed algorithm
for geographically distributed data sets that reduces communication
costs. Recently, as the need to mine patterns across distributed
databases has grown, Distributed Association Rule Mining (D-ARM)
algorithms have been developed. These algorithms, however, assume
that the databases are either horizontally or vertically distributed. In
the special case of databases populated from information extracted
from textual data, existing D-ARM algorithms cannot discover rules
based on higher-order associations between items in distributed
textual
documents
that
are
neither
vertically
nor
horizontally
distributed, but rather a hybrid of the two.
Modern organizations are geographically distributed. Typically,
each site locally stores its ever increasing amount of day-to-day data.
Using centralized data mining to discover useful patterns in such
organizations' data isn't always feasible because merging data sets

10. from different sites into a centralized site incurs huge network
communication costs. Data from these organizations are not only
distributed over various locations but also vertically fragmented,
making it difficult if not impossible to combine them in a central
location. Distributed data mining has thus emerged as an active
subarea of data mining research.
A significant area of data mining research is association rule
mining. Unfortunately, most ARM algorithms focus on a sequential or
centralized environment where no external communication is required.
Distributed ARM algorithms, on the other hand, aim to generate rules
from different data sets spread over various geographical sites; hence,
they require external communications throughout the entire process.
DARM algorithms must reduce communication costs so that generating
global association rules costs less than combining the participating
sites' data sets into a centralized site. However, most DARM
algorithms don't have an efficient message optimization technique, so
they exchange numerous messages during the mining process. We
have developed a distributed algorithm, called Optimized Distributed
Association Mining, for geographically distributed data sets. ODAM
generates support counts of candidate itemsets quicker than other
DARM algorithms and reduces the size of average transactions, data
sets, and message exchanges.

11. Description of Problem
After the advent of computer the data are enormously available
and by making use of such raw collection data to invent the knowledge
is the process of Data Mining. Like wise in Web also plenty of Web
Documents resides in online. Web is repository of variety of
information like Technology, Science, History, Geography, Sports
Politics and others. If any one know about particular topic, then they
are using search engine to search for their requirements and it gives
full satisfaction for that user by giving entire related information about
the topic. We can categorize parallel ARM algorithms as dataparallelism or task-parallelism algorithms. In the former, the
algorithms partition the data sets among different nodes; in the latter,
each site performs the task independently but must access the entire
data set. The Count Distribution (CD) algorithm is a simple dataparallelism algorithm.2 It uses the sequential Apriori algorithm in a
parallel environment and assumes data sets are horizontally
partitioned among different sites.
DARM discovers rules from various geographically distributed data
sets. However, the network connection between those data sets isn't
as fast as in a parallel environment, so distributed mining usually aims
to minimize communication costs.

12. Existing Method
The Data mining Algorithms can be categorized into the following
:

Association Algorithm

Classification

Clustering Algorithm
Classification:
The process of dividing a dataset into mutually exclusive groups
such that the members of each group are as "close" as possible to one
another, and different groups are as "far" as possible from one
another, where distance is measured with respect to specific
variable(s) you are trying to predict. For example, a typical
classification problem is to divide a database of companies into groups
that are as homogeneous as possible with respect to a
creditworthiness variable with values "Good" and "Bad."
Clustering:
The process of dividing a dataset into mutually exclusive groups
such that the members of each group are as "close" as possible to one
another, and different groups are as "far" as possible from one
another, where distance is measured with respect to all available
variables.

13. Given databases of sufficient size and quality, data mining technology
can generate new business opportunities by providing these
capabilities:
•
Automated prediction of trends and behaviors. Data mining
automates the process of finding predictive information in large
databases. Questions that traditionally required extensive handson analysis can now be answered directly from the data —
quickly. A typical example of a predictive problem is targeted
marketing. Data mining uses data on past promotional mailings
to identify the targets most likely to maximize return on
investment in future mailings. Other predictive problems include
forecasting bankruptcy and other forms of default, and
identifying segments of a population likely to respond similarly to
given events.
•
Automated discovery of previously unknown patterns.
Data mining tools sweep through databases and identify
previously hidden patterns in one step. An example of pattern
discovery is the analysis of retail sales data to identify seemingly
unrelated products that are often purchased together. Other
pattern discovery problems include detecting fraudulent credit

14. card transactions and identifying anomalous data that could
represent data entry keying errors.
DARM discovers rules from various geographically distributed data
sets. However, the network connection between those data sets isn't
as fast as in a parallel environment, so distributed mining usually aims
to minimize communication costs.
Proposed System
Unlike other algorithms, ODAM offers better performance by
minimizing candidate itemset generation costs. It achieves this by
focusing on two major DARM issues communication and
synchronization. Communication is one of the most important DARM
objectives. DARM algorithms will perform better if we can reduce
communication (for example, message exchange size) costs.
Synchronization forces
each participating site to wait a certain period until globally frequent
itemset generation completes. Each site will wait longer if computing
support counts takes more time. Hence, we reduce the computation
time of candidate itemsets' support counts.
To reduce communication costs, we highlight several message
optimization techniques. ARM algorithms and on the message

15. exchange method, we can divide the message optimization techniques
into two methods direct and indirect support counts exchange. Each
method has different aims, expectations, advantages, and
disadvantages. For example, the first method exchanges each
candidate itemset's support count to generate globally frequent
itemsets of that pass (CD and FDM are examples of this approach). All
sites share a common globally frequent itemset with identical support
counts, so rules that are generated at different participating sites have
identical confidence. This approach focuses on a rule's exactness and
correctness.

16. System Requirement
Hardware specifications:
Processor
RAM
:
:
Intel Processor IV
128 MB
Hard disk
:
20 GB
CD drive
:
40 x Samsung
Floppy drive
:
1.44 MB
Monitor
:
15’ Samtron color
Keyboard
Mouse
:
:
108 mercury keyboard
Logitech mouse
Software Specification
Operating System – Windows XP/2000
Language used – J2sdk1.4.0, JCreator