With an expontial growth of World Wide Web, there are so many information overloaded and it became hard to find out data according to need. Web usage mining is a part of web mining, which deal with automatic discovery of user navigation pattern from web log. This paper presents an overview of web mining and also provide navigation pattern from classification and clustering algorithm for web usage mining. Web usage mining contain three important task namely data preprocessing, pattern discovery and pattern analysis based on discovered pattern. And also contain the comparative study of web mining techniques.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
The World Wide Web (Web) is a popular and interactive medium to disseminate information today.
The Web is huge, diverse, and dynamic and thus raises the scalability, multi-media data, and temporal issues respectively.
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Web mining is the application of data mining techniques to discover patterns from the World Wide Web. As the name proposes, this is information gathered by mining the web
BIDIRECTIONAL GROWTH BASED MINING AND CYCLIC BEHAVIOUR ANALYSIS OF WEB SEQUEN...ijdkp
Web sequential patterns are important for analyzing and understanding users’ behaviour to improve the
quality of service offered by the World Wide Web. Web Prefetching is one such technique that utilizes
prefetching rules derived through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we use a highly efficient and
scalable mining technique such as the Bidirectional Growth based Directed Acyclic Graph. In this paper,
we propose a novel algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of web
sequential Patterns (BGCAP) that effectively combines these strategies to generate prefetching rules in the
form of 2-sequence patterns with Periodicity and threshold of Cyclic Behaviour that can be utilized to
effectively prefetch Web pages, thus reducing the users’ perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion for mining n Web sequential
patterns. Our experimental results show that prefetching rules generated using BGCAP is 5-10% faster for
different data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition, BGCAP generates
about 5-15% more prefetching rules than TD-Mine.
Web mining tools based on content mining,usage mining and structure mining. Tools : Tableau,R, Octoparse , Scrapy, Hits and Pagerank algo. also included.
An Enhanced Approach for Detecting User's Behavior Applying Country-Wise Loca...IJSRD
The development of the web in past few years has created a lot of challenge in this field. The new work in this field is the search of the data in a search tree pattern based on tree. Various sequential mining algorithms have been devoloped till date. Web usage mining is used to operate the web server logs, that contains the navigation history of the user. Recommendater system is explained properly with the explanation of whole procedure of the recommendater system. The search results of the data leads to the proper ad efficient search. But the problem was the time utilization and the search results generated from them. So, a new local search algorithm is proposed for country-wise search that makes the searching more efficient on local results basis. This approach has lead to an advancement in the search based methods and the results generated.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting
valuable information from web databases. Web mining is one of the data mining domains where data
mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer
behaviour, evaluate a particular website based on the information which is stored in web log files. Web
mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library.
Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase
the complexity of dealing information from different web service providers. The collection of information
becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
Web is a collection of inter-related files on one or more web servers while web mining means extracting valuable information from web databases. Web mining is one of the data mining domains where data mining techniques are used for extracting information from the web servers. The web data includes web
pages, web links, objects on the web and web logs. Web mining is used to understand the customer behaviour, evaluate a particular website based on the information which is stored in web log files. Web mining is evaluated by using data mining techniques, namely classification, clustering, and association
rules. It has some beneficial areas or applications such as Electronic commerce, E-learning, Egovernment, E-policies, E-democracy, Electronic business, security, crime investigation and digital library. Retrieving the required web page from the web efficiently and effectively becomes a challenging task
because web is made up of unstructured data, which delivers the large amount of information and increase the complexity of dealing information from different web service providers. The collection of information becomes very hard to find, extract, filter or evaluate the relevant information for the users. In this paper,
we have studied the basic concepts of web mining, classification, processes and issues. In addition to this,
this paper also analyzed the web mining research challenges.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Recommendation generation by integrating sequential pattern mining and semanticseSAT Journals
Abstract As the Internet usage keeps increasing, the number of web sites and hence the number of web pages also keeps increasing. A recommendation system can be used to provide personalized web service by suggesting the pages that are likely to be accessed in future. Most of the recommendation systems are based on association rule mining or based on keywords. Using the association rule mining the prediction rate is less as it doesn’t take into account the order of access of the web pages by the users. The recommendation systems that are key-word based provides lesser relevant results. This paper proposes a recommendation system that uses the advantages of sequential pattern mining and semantics over the association rule mining and keyword based systems respectively. Keywords: Sequential Pattern Mining, Taxonomy, Apriori-All, CS-Mine, Semantic, Clustering
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Review on Pattern Discovery Techniques of Web Usage MiningIJERA Editor
In the recent years with the development of Internet technology the growth of World Wide Web exceeded all expectations. A lot of information is available in different formats and retrieving interesting content has become a very difficult task. One possible approach to solve this problem is Web Usage Mining (WUM), the important application of Web Mining. Extracting the hidden knowledge in the log files of a web server, recognizing various interests of web users, discovering customer behavior while at the site are normally referred as the applications of web usage mining. In this paper we provide an updated focused survey on techniques of web usage mining.
Web Page Recommendation Using Web MiningIJERA Editor
On World Wide Web various kind of content are generated in huge amount, so to give relevant result to user web recommendation become important part of web application. On web different kind of web recommendation are made available to user every day that includes Image, Video, Audio, query suggestion and web page. In this paper we are aiming at providing framework for web page recommendation. 1) First we describe the basics of web mining, types of web mining. 2) Details of each web mining technique.3)We propose the architecture for the personalized web page recommendation.
World Wide Web is a huge repository of information and there is a tremendous increase in the volume of
information daily. The number of users are also increasing day by day. To reduce users browsing time lot
of research is taken place. Web Usage Mining is a type of web mining in which mining techniques are
applied in log data to extract the behaviour of users. Clustering plays an important role in a broad range
of applications like Web analysis, CRM, marketing, medical diagnostics, computational biology, and many
others. Clustering is the grouping of similar instances or objects. The key factor for clustering is some sort
of measure that can determine whether two objects are similar or dissimilar. In this paper a novel
clustering method to partition user sessions into accurate clusters is discussed. The accuracy and various
performance measures of the proposed algorithm shows that the proposed method is a better method for
web log mining.
Similar to Web Usage Mining: A Survey on User's Navigation Pattern from Web Logs (20)
Due to availability of internet and evolution of embedded devices, Internet of things can be useful to contribute in energy domain. The Internet of Things (IoT) will deliver a smarter grid to enable more information and connectivity throughout the infrastructure and to homes. Through the IoT, consumers, manufacturers and utility providers will come across new ways to manage devices and ultimately conserve resources and save money by using smart meters, home gateways, smart plugs and connected appliances. The future smart home, various devices will be able to measure and share their energy consumption, and actively participate in house-wide or building wide energy management systems. This paper discusses the different approaches being taken worldwide to connect the smart grid. Full system solutions can be developed by combining hardware and software to address some of the challenges in building a smarter and more connected smart grid.
A Survey Report on : Security & Challenges in Internet of Thingsijsrd.com
In the era of computing technology, Internet of Things (IoT) devices are now popular in each and every domains like e-governance, e-Health, e-Home, e-Commerce, and e-Trafficking etc. Iot is spreading from small to large applications in all fields like Smart Cities, Smart Grids, Smart Transportation. As on one side IoT provide facilities and services for the society. On the other hand, IoT security is also a crucial issues.IoT security is an area which totally concerned for giving security to connected devices and networks in the IoT .As, IoT is vast area with usability, performance, security, and reliability as a major challenges in it. The growth of the IoT is exponentially increases as driven by market pressures, which proportionally increases the security threats involved in IoT The relationship between the security and billions of devices connecting to the Internet cannot be described with existing mathematical methods. In this paper, we explore the opportunities possible in the IoT with security threats and challenges associated with it.
In today’s emerging world of Internet, each and every thing is supposed to be in connected mode with the help of billions of smart devices. By connecting all the devises used in our day to day life, make our life trouble less and easy. We are incorporated in a world where we are used to have smart phones, smart cars, smart gadgets, smart homes and smart cities. Different institutes and researchers are working for creating a smart world for us but real question which we need to emphasis on is how to make dumb devises talk with uncommon hardware and communication technology. For the same what kind of mechanism to use with various protocols and less human interaction. The purpose is to provide the key area for application of IoT and a platform on which various devices having different mechanism and protocols can communicate with an integrated architecture.
Study on Issues in Managing and Protecting Data of IOTijsrd.com
This paper discusses variety of issues for preserving and managing data produced by IoT. Every second large amount of data are added or updated in the IoT databases across the heterogeneous environment. While managing the data each phase of data processing for IoT data is exigent like storing data, querying, indexing, transaction management and failure handling. We also refer to the problem of data integration and protection as data requires to be fit in single layout and travel securely as they arrive in the pool from diversified sources in different structure. Finally, we confer a standardized pathway to manage and to defend data in consistent manner.
Interactive Technologies for Improving Quality of Education to Build Collabor...ijsrd.com
Today with advancement in Information Communication Technology (ICT) the way the education is being delivered is seeing a paradigm shift from boring classroom lectures to interactive applications such as 2-D and 3-D learning content, animations, live videos, response systems, interactive panels, education games, virtual laboratories and collaborative research (data gathering and analysis) etc. Engineering is emerging with more innovative solutions in the field of education and bringing out their innovative products to improve education delivery. The academic institutes which were once hesitant to use such technology are now looking forward to such innovations. They are adopting the new ways as they are realizing the vast benefits of using such methods and technology. The benefits are better comprehensibility, improved learning efficiency of students, and access to vast knowledge resources, geographical reach, quick feedback, accountability and quality research. This paper focuses on how engineering can leverage the latest technology and build a collaborative learning environment which can then be integrated with the national e-learning grid.
Internet of Things - Paradigm Shift of Future Internet Application for Specia...ijsrd.com
In the world more than 15% people are living with disability that also include children below age of 10 years. Due to lack of independent support services specially abled (handicap) people overly rely on other people for their basic needs, that excludes them from being financially and socially active. The Internet of Things (IoT) can give support system and a better quality of life as well as participation in routine and day to day life. For this purpose, the future solutions for current problems has been introduced in this paper. Daunting challenges have been considered as future research and glimpse of the IoT for specially abled person is given in the paper.
A Study of the Adverse Effects of IoT on Student's Lifeijsrd.com
Internet of things (IoT) is the most powerful invention and if used in the positive direction, internet can prove to be very productive. But, now a days, due to the social networking sites such as Face book, WhatsApp, twitter, hike etc. internet is producing adverse effects on the student life, especially those students studying at college Level. As it is rightly said, something which has some positive effects also has some of the negative effects on the other hand. In this article, we are discussing some adverse effects of IoT on student’s life.
Pedagogy for Effective use of ICT in English Language Learningijsrd.com
The use of information and communications technology (ICT) in education is a relatively new phenomenon and it has been the educational researchers' focus of attention for more than two decades. Educators and researchers examine the challenges of using ICT and think of new ways to integrate ICT into the curriculum. However, there are some barriers for the teachers that prevent them to use ICT in the classroom and develop supporting materials through ICT. The purpose of this study is to examine the high school English teachers’ perceptions of the factors discouraging teachers to use ICT in the classroom.
In recent years usage of private vehicles create urban traffic more and more crowded. As result traffic becomes one of the important problems in big cities in all over the world. Some of the traffic concerns are traffic jam and accidents which have caused a huge waste of time, more fuel consumption and more pollution. Time is very important parameter in routine life. The main problem faced by the people is real time routing. Our solution Virtual Eye will provide the current updates as in the real time scenario of the specific route. This research paper presents smart traffic navigation system, based on Internet of Things, which is featured by low cost, high compatibility, easy to upgrade, to replace traditional traffic management system and the proposed system can improve road traffic tremendously.
Ontological Model of Educational Programs in Computer Science (Bachelor and M...ijsrd.com
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Understanding IoT Management for Smart Refrigeratorijsrd.com
Lately the concept of Internet of Things (IoT) is being more elaborated and devices and databases are proposed thereby to meet the need of an Internet of Things scenario. IoT is being considered to be an integral part of smart house where devices will be connected to each other and also react upon certain environmental input. This will eventually include the home refrigerator, air conditioner, lights, heater and such other home appliances. Therefore, we focus our research on the database part for such an IoT’ fridge which we called as smart Fridge. We describe the potentials achievable through a database for an IoT refrigerator to manage the refrigerator food and also aid the creation of a monthly budget of the house for a family. The paper aims at the data management issue based on a proposed design for an intelligent refrigerator leveraging the sensor technology and the wireless communication technology. The refrigerator which identifies products by reading the barcodes or RFID tags is proposed to order the required products by connecting to the Internet. Thus the goal of this paper is to minimize human interaction to maintain the daily life events.
DESIGN AND ANALYSIS OF DOUBLE WISHBONE SUSPENSION SYSTEM USING FINITE ELEMENT...ijsrd.com
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Connecting rod is very important part of engine. It should be accurately machined with the acceptable tolerance. Also the fluctuations of dimensions in work-piece to work piece should be minimum so that it will be easier to assemble in engine. But it has been observed that the required dimensions for the bolt diameter and smaller end diameter for the connecting rod are not continuously achievable by using the existing fixture. The diameters required of the bolts and the smaller end of the said connecting rod are 10±0.05 mm and 24±0.01 mm respectively The aim of this project is to design and development of a new fixture for machining (Boring) operation using designing software's i.e. Pro E and analysis using ANSYS ,which can eliminate the said problems. And the production rate will also increase up to 15% which is quite objective. So for that, a new hydraulic fixture is designed and observed that dimensional accuracy, increased production rate up to 15% and more output per day with boring operation. Which defines process is satisfactory enough and validates the project.
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Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
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The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
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Web Usage Mining: A Survey on User's Navigation Pattern from Web Logs
1. IJSRD - International Journal for Scientific Research & Development| Vol. 2, Issue 09, 2014 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 567
Web Usage Mining: A Survey on User’s Navigation Pattern from Web
Logs
Richa Patel1 Akshay Kansara2
1
P.G. Student 2
Assistant Professor
1,2
Department of Computer Science and Engineering
1,2
Mehasana, India
Abstract— With an expontial growth of World Wide Web,
there are so many information overloaded and it became
hard to find out data according to need. Web usage mining
is a part of web mining, which deal with automatic
discovery of user navigation pattern from web log. This
paper presents an overview of web mining and also provide
navigation pattern from classification and clustering
algorithm for web usage mining. Web usage mining contain
three important task namely data preprocessing, pattern
discovery and pattern analysis based on discovered pattern.
And also contain the comparative study of web mining
techniques.
Key words: web mining, web usage mining, web log data,
classification, clustering
I. INTRODUCTION
In today’s world internet has become extremely popular and
its growth is very rapid. The information is available on
internet for people whom they are using for their different
intend. The resources for using internet are growing fast, it
is necessary for users to use automatic tools for discover
desired information. People require systems at client side
and server side for finding out the desired information.
Above system used to mine data and extract information
from that source. So for a specific user only interesting
information of web is useful and rest of the information not
important. Several users are interested in content of web and
they can browse by using search engines. Using web log
files, we can find out information related to web access
pattern. These web log files provide the information related
to behavior of user. In business area, user’s behavior plays
an important role for extracting information.
In this area, user navigation patterns are described
as the common browsing behaviors along with a group of
users. During navigation, many users may have common
interests .so navigation patterns should capture the
overloaded information or user’s need. In addition,
navigation patterns should also be able to differentiate
among web pages based on their different meaning to each
pattern.
The rest of paper is as follows: Section II presents
the overview of web mining and types of web mining.
Section III related to literature work. Section IV explains the
comparisons of WCM, WSM and WUM. Section V
represent conclusion.
II. WEB MINING
A. Overview
Web mining is a part of data mining techniques to
automatically discover and extract knowledge from the web.
It can be broadly divided into three tasks: web content
mining, web usage mining, web structure mining.
Classification of Web Mining can be understood using
the given Fig .1
Fig.1: Web Mining Classification
Web Mining is an application of Data Mining
which deals with the extraction of interesting pattern from
the World Wide Web. In above figure web mining can be
classified into three categories: web structure mining, web
content mining and web usage mining. Web Structure
Mining is the process of inferring knowledge from the
World Wide Web and links between web pages. The
structure of a typical web graph contains web pages as
nodes and hyperlinks as edges between related web pages. It
is the process of using graph theory to examine the node and
connection structure of a web site. Web content mining is
the process of extracting useful information from the
available web pages. Generally, the web content mining
contain the several types of source data such as textual,
image, audio, video, metadata as well as hyperlinks. Web
Usage mining is a part of data mining techniques to discover
useful navigation patterns from web log. Resource data is
usually collected when user interact with web server such as
web server log, proxy server logs, user queries, registration
data. In short, Web usage mining is a process of extracts
information from user how to use web sites. Web content
mining is a process of extracts information from texts,
images and other contents. Web structure mining is a
process of extracts information from hyperlinks of web
pages.
B. Web Usage Mining
Web usage mining is an part of web mining. It contain the
two categories, first is general access pattern tracking and
second is customized usage tracking. Now, general access
pattern is a mining process using the history of the web page
visited by user. And customized usage tracking is targeted
on specific user. Web usage mining, the art of analyzing
user interactions with a web page, has been dealt by several
researchers using different approaches.[1] there are many
research area in data mining techniques like association rule
mining, classification ,clustering and Sequential-pattern-
mining-based.
C. Web Usage Mining Techniques
These techniques given below:
2. Web Usage Mining: A Survey on User’s Navigation Pattern from Web Logs
(IJSRD/Vol. 2/Issue 09/2014/129)
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1) Sequential-pattern-mining-based: Allows the
discovery of temporally ordered Web access
patterns,
2) Association-rule-mining-based: Finds correlations
among Web pages,
3) Clustering-based: Groups users with similar
characteristics,
4) Classification-based: Groups users into predefined
classes based on their characteristics [2].
Here all are given briefly:
1) Association Rules Mining:
Association rule mining can be used for related pages that
are most often referenced together in a single server
sessions. These rules refer to sets of pages that are accessed
together with a support value more than some predefined
threshold. Association rule is a concept of data mining that
is used for basket transaction data analysis. There are
several association rule algorithms have been used, such as
Apriori, Partition. This rule is being applicable for business
intelligence and marketing applications, e-Commerce and
also it can help web designers to restructure their web site.
2) Sequential Pattern Mining:
A database consists of sequences of events or values that
change with time, is called a time-series database. Time-
series database is widely used to store historical data in a
variety of areas such as, scientific data, financial data,
medical data, and so on. Different mining techniques have
been designed for mining time-series data, basically there
are four kinds of patterns we can get from various types of
time series data:1) Trend analysis, 2) Sequential pattern,
3)Similarity search, and 4) Periodical patterns. This
technique of sequential pattern mining attempts to find the
relationships between occurrences of sequential events, and
also to find if there exists any specific order of the
occurrences We can find the sequential patterns of cross
different items and also find the specific individual items.
Generally, Sequential pattern mining is used in analyzing of
DNA sequence. An example of sequential patterns is that
every time IBM stock drops at least 4%, Microsoft stock
will drops 5% within three days.
3) Classification:
Classification is a supervised learning technique, to
automatically generate a model that can be classify as a
class of objects so that to predict the classification or
missing attribute value of future objects whose class may
not be known. Classification is a two-step process. In the
first step is model construction, it is based on the collection
of training data set, a model is constructed to describe the
predetermine characteristics of a set of data classes or
concepts. Class label of training data is known is called
supervised learning (i.e., which class the training sample
belongs to is provided). In the second step, model usage is
used to predict the classes of future objects or data. .
Classification can be done by using several learning
algorithms such as decision tree classifiers, k-nearest
neighbor classifiers, naïve Bayesian classifiers, Support
Vector Machines etc. The decision tree induction
Classification algorithms such as C4.5, ID3, CART, and
Hunt’s algorithm can be used to predict if page is of interest
to the user.
4) Clustering:
Classification is a supervised learning technique, clustering
is another mining technique similar to classification.
However clustering is an unsupervised learning technique.
Clustering is a technique in which set of data item or object
having similar characteristics they are group together.
Objects characteristics are same within a cluster but
dissimilar to characteristics of object in other clusters.
Clustering can be performed on either the page views or the
user views. Clustering analysis in web usage mining intends
to find the cluster of page, user, or sessions from web log
file, where each cluster represents a group of objects with
common interesting or characteristic. The clustering method
are model-based, partition based, grid based, hierarchical
based. To construct partition of data, we use partition
method like k-mean, k-medoid. Each partition represent
cluster.
5) Web Log Data
Log files can be classified into three categories:
Web Server Log Files: Web server log files located
in web server and notes activity of the user
browsing website. There are four types of web
server logs i.e., transfer logs, error logs, agent logs,
and referrer logs.
Web Proxy Server Log Files: web proxy server log
files contain information about the proxy server
from which user request came to the web server.
Client browser Log Files: client browser log files
reside in client’s browser and to store them special
software are used.
6) Log Files Parameters
To recognizing user browsing patterns, they are use log files
parameters which are given below.
User Name: To identify the user who has visited
the website and this identification normally is IP
address.
Path Traversed: it is the path taken by the user
within the website.
Visiting Path: in which path taken by the user while
visiting the website.
Time Stamp: It is the time spends by user on each
page and is normally known as session.
Page Last Visited: It is the last page visited by the
user while leaving the website.
Success Rate: It is measured by copying and
downloads activity carried out on the website.
User Agent: It is the browser that user send request
to server.
URL: It is the resource that is accessed by the user
and it may be of any format like CGI, HTML etc.
Request Type: It is the method that is used by user
to send request to the server and it can be either
GET or POST method.
a) Types of Log File Format
There are mainly three kinds of log file formats that are used
by majority of the servers.
Common Log File Format: It is the standardized
text file format that is used by most of the web servers to
generate the log files.[16]
Format is given below:
3. Web Usage Mining: A Survey on User’s Navigation Pattern from Web Logs
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Log Format "%h %l %u %t "%r" %>s %b"
common CustomLog logs/access_log common eg:
127.0.0.1 RFC 1413 frank [10/Oct/2000:13:55:36 -
0700] "GET /apache_pb.gif HTTP/1.0" 200
2326[16]
Combined Log Format: It is same as the common
log file format but with three additional fields i.e.,
referral field, the user_agent field, and the cookie
field.[16]
Format is given below:
Log Format "%h %l %u %t "%r" %>s %b
"%{Referer}i" "%{Useragent i"" combined CustomLog
log/access_log combined eg: 127.0.0.1 - frank
[10/Oct/2000:13:55:36 -0700] "GET /apache_pb.gif
HTTP/1.0" 200 2326 "http://www.example.com/start.html"
"Mozilla/4.08 [en] (Win98; I;Nav)”[16]
Multiple Access Logs: It is the combination of
common log format and combined log file format
but in this format multiple directories can be
created for access logs.[16]
Format is given below:
LogFormat "%h %l %u %t "%r" %>s %b" common
CustomLog logs/access_log common CustomLog
logs/referer_log "%{Referer}i -> %U" CustomLog
logs/agent_log "%{User-agent}i"[16]
The above techniques help in the development of
effective marketing strategies as well as design of better
Web sites. Generally, users are not ready to disclose
personal information and may tend to give false information
to the websites. Hence, it is assumed that the anonymity of
users for WUM in general, particularly for non-commercial
web sites where user registration is not required, and
disregards clustering and classification.
D. Web Usage Mining Steps
There are several steps in web usage mining given in fig.2
Fig. 2: stages of preprocessing [3]
The main objectives of preprocessing are to reduce
the size of data and at a same time quality being enhanced.
Preprocessing contain the following steps – Merging of Log
files from Different Web Servers, Data cleaning, User
Identification, Session Identification, Data formatting and
Summarization as shown in Fig. 2.
1) Merging
At the beginning of the data preprocessing is merging, the
request from the all web log files, merging all log files with
web server name and synchronization of web server clocks,
including time zone differences.
2) Data Cleaning
The second step of data preprocessing is data cleaning, it
consists of remove unnecessary request from web server log
files. We need to eliminate the irrelevant entries if all web
log entries are not valid. Usually, this process remove
request which contain non-analyzed data such as images,
multimedia files and page style files.
3) User Identification
In generally, the web log file contains only the computer
address (name or IP address) and the user agent (for ECLF
log files). The web log file also contains the user login that
can be used for the user identification when user login or
register in particular web sites.
4) Session Identification
To group the activities of a single user from the web log
files is called a session [4]. A user session is called when as
longer as user connected to particular web sites. Default
session taken as a 30 minutes time-out. A session is a set of
reference pages from one web site during one logical time
period. Historically session identification is done by a user
logging into a computer, performing the task and then
logging off. The login and logoff represent the logical time
period of the start session and end of the session.
5) Data Formatting & Summarization
This is the last step of data preprocessing, the structured or
formatted file containing user sessions and visits are
transformed into a relational database model.
III. LITERATURE REVIEW
The focus of literature review is to study and analyze about
available technique to predict the users' navigation pattern
with the web or web site. . There are many Web log analysis
tools presented for mine the data from log record on Web
page. Log record provides useful information likes IP
address, URL and time and so on. Analyzing and
discovering of log could help organizations to discovered
more potential customers, pages popularity number of times
a page has been visited etc. that can help in reorganizing the
Web site for fast and easy customer access, , attracting more
advertisement capital by intelligent adverts improving links
and navigation, turning viewers into customers by better
site architecture.
Internet is a gold mine, but only for those
companies who realize the importance of Web mining and
adopt a Web mining strategy now.[5] Generally, Companies
have to implement Web mining systems to identify their
own strength and weakness , and to understand their
customers' profiles of their E-marketing efforts on the web
through continuous improvements. In this paper author
describe the overview of web mining and types and
application of web mining. From the data analysis and
graphing workspace tool, we are helpful in providing useful
information related to the user access patterns, which could
not be possible by using traditional approaches.[6] in this
paper they considered university’s peak working time for
analysis of web traffic. Traffic data was selected based on
daily, monthly and hourly including request volume and
page volume to generate cluster models. In web usage
mining grouping of web access sequences can be used to
determine the behavior or intent of a set of users.[7] The
task of grouping web sessions based on similarity and
consists of maximizing the intra-group similarity while
4. Web Usage Mining: A Survey on User’s Navigation Pattern from Web Logs
(IJSRD/Vol. 2/Issue 09/2014/129)
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minimizing the inter-group similarity is done using sequence
alignment method.[7] In this paper author describe a new
method to group web sessions, which considers the global
and local alignment techniques of similarity measurement..
Length of session also plays an important role for session.
Sequence alignment method can be improved by affine gaps
in computing global alignment value .we can also find the
web session similarity based on Fuzzy Rough Set Theory. In
that, rough set theory defines the characteristics of each
cluster. Web site graph contain vertices of web pages and
edges of link between web pages. Using Table Filling
Algorithm to generate the cluster Traditional clustering
methods create clusters by describing the members of each
cluster whereas the rough set based clustering techniques
create clusters describing the main characteristics of each
cluster.[9] [8], There is an attempt to provide an overview of
the state of the art in the research of web usage mining,
while discussing the most relevant tools available in the
sphere as well as the niche requirements that the current
variety of tools lack. All tools are related to statistical
analysis techniques, likes WEBMINER, Web Tool, Web
Mate and so on. The main uses of web content mining are to
gather, categorize, organize and provide the best possible
information available on the Web to the user requesting the
information [17]. In this paper web content mining tool scan
the HTML documents, text and images. They compare the
web content mining tools like screen-scraper, Automation
Anywhere 6.1, Web Info Extractor, Mozenda, and Web
Content Extractor. Screen-scrapper needs prior knowledge
of proxy server and some knowledge of HTML and HTTP
where as other tools do not require any such knowledge and
it need Internet connection to run [17]. Web contains noisy
data, redundant information and which mirrored web pages
in and abundance [18]. They use web content extraction
method to remove noisy data present in web documents.
They use three phases to perform web content extraction
method. First phase list of documents are selected, second
phase documents are preprocessed, and in third phase result
are presented to user. Proposed method shows better
performance when compared with existing methods [18].
Web content mining tool and techniques and analyzed the
method for retrieving information. This method focuses on
the following objectives: Focusing on the role of web
content extraction and identifying list problems when
mining list of documents. Studying the solutions to this
problem, presenting the method which is used to identify
required patterns in an effective manner [18] Examining a
Web content data consist of structured, semi – structured
and un- structured data. Web content mining techniques
such as text based clustering, partition clustering,
hierarchical clustering, graph based clustering, neural
network based clustering , fuzzy clustering and probabilistic
clustering. And they also describe the research issue on web
content mining such as data/information extraction in that
product or result search extraction, web information
integration and schema matching, opinion extraction from
online sources in that customer review of products, forums,
blogs, and chat rooms .knowledge synthesis, segmenting
web pages and detecting noise in that like content of web
page without advertisement, navigation links and copyrights
notices. An EM algorithm is used in statistics for finding
maximum likelihood estimates of parameters in probabilistic
models, where the model depends on unobserved latent
variables.[10]This algorithm is a type of partitioning
clustering method and it is a mixture of Markov chain used
for clustering user session. In this algorithm there are two
steps, first is expectation step in which value is expected
from cluster probabilities and second is maximization step
in which they compute distribution parameters and their like
hood. This algorithm work same as k-mean in that set of
parameters are re-computed until desired value is achieved.
This algorithm used for improve cluster quality. Machine
learning research area have two activity first is clustering
and second is classification. Both the classification and
clustering algorithm are used to find the maximum and
minimum likelihood estimates of parameters in probabilistic
models, where the model depends on unobserved latent
variables.[11]In this paper ,author use graph partitioned
clustering algorithm to discover the navigation pattern and
LCS classification algorithm to predict future requests.
After applying clustering and classification three
performance parameters accuracy, coverage and F1 Measure
are measured. They provide accuracy 0.87% when threshold
was 0.9.To predict future request and finding navigation
pattern we use clustering and classification techniques. In
this paper there are two phase, one is online phase and
second is offline phase. The offline phase takes care of
preprocessing and clustering, while the classification and
prediction is performed during the online phase [12].Ant-
based clustering method used to discover or extract user’s
navigational pattern from web log files. The LCS
classification algorithm uses the knowledge from offline
stage and predicts the users’ next request [12]. After
applying clustering and classification they measure
maximum accuracy 74% when threshold value increases. In
this paper we deal with classification algorithms for
studying the user/client behavior and for the generation of
interested user patterns [13]. In this paper they give the
comparison of classification algorithms based on some
factor like accuracy, precision, session based timing and
recall. Classification algorithms are to be applied on the web
log data and then performance of these algorithms can be
measured. Here classification algorithm likes Decision Tree
Classifier, Naive Byes Classifier, Support Vector Machine,
Neural Networks, Rule Based Classifier, and K-Nearest
Neighbor Classifier are analyzed. After analyzing above
algorithms, author say that the Decision Tree classifier and
SVM algorithm are perform well as compared to others.
Clustering is useful in characterizing customer groups based
on purchasing patterns, categorizing web documents that
have similar functionality [14]. In this paper they uses graph
based clustering algorithm which is Chameleon clustering
algorithm, it takes data point as an input and forms
clusters. They uses adjacency matrix in which session as
rows and pages as column. This matrix generates the data
point which is input to algorithm. This algorithm works
same as KNN graph. It has two phases. In first phase, they
use graph partition algorithm to partition cluster into small
sub-clusters. And in second phase, they merge the cluster
until relative number of cluster is achieved. This algorithm
provides both interconnectivity and closeness to identifying
the most similar pairs of cluster.
5. Web Usage Mining: A Survey on User’s Navigation Pattern from Web Logs
(IJSRD/Vol. 2/Issue 09/2014/129)
All rights reserved by www.ijsrd.com 571
IV. COMPARATIVE STUDY OF WCM, WSM AND WUM
Here, given table represent the comparison of WCM, WSM
and WUM:
Specification WCM WSM WUM
Tasks
information
from the
web content /
documents
It discover the
model
underlying the
link structure
of the web
It tries to
make sense
of data
generated by
web surfer’s
session or
behavior
Input Data primary data
It uses primary
data
It uses
secondary
data
Data View
structured,
semi-
structured,
unstructured
Linking of
structure
Interactive
Data
Method
Machine
Learning,
statistical
Method,
Association
Rule,
Proprietary
algorithm
Proprietary
algorithm
Statistical
Method,
Machine
Learning
Application
Categories
Finding
Extraction
Rules,
Finding Pattern
in text,
Finding
Frequent Sub-
structure,
Categorization,
Clustering
Categorization,
Clustering
Site
Construction,
Adaption
and
Management,
Marketing
User
Modeling
Outcome
Bag of word,
Phrases, Edge
labeled graph
Graph
Relational
Table, Graph
V. CONCLUSION
This paper provide a more current evolution and of Web
Usage Mining. It describes the user navigation pattern from
classification and clustering algorithm. Web Usage Mining
is a fast rising research area for generating interested log
information. And also provide the comparison table of Web
Content Mining, Web Structure Mining and Web Usage
Mining.
REFERENCES
[1] Niranjana. Kannan and Dr. Elizabeth Shanthi,
“Classification and Clustering of Web Log Data to
Analyze User Navigation Patterns”, JGRCS,
Volume 1, No. 1, August 2010
[2] Yew-Kwong Woon ,Wee-Keong Ng, Ee-Peng
Lim,“Web Usage Mining: Algorithms and Results”
[3] Ramya C, Kavitha G, “An Efficient Preprocessing
Methodology for Discovering Patterns and
Clustering of Web Users using a Dynamic ART1
Neural Network”, Fifth International Conference
on Information Processing, Augus-2011,Bangalore,
INDIA
[4] Vijayashri Losarwar, Dr. Madhuri Joshi, “Data
Preprocessing in Web Usage Mining”,
ICAIES'2012,July 15-16, 2012
[5] Monika Yadav, Pradeep Mittal, “Web Mining: An
Introduction”, IJARCSSE, Volume 3, Issue 3,
March 2013
[6] Shakti Kundu, “An Intelligent Approach Of Web
Data Mining”, IJCSE, Vol. 4 No. 05 May 2012
[7] Bhupendra S Chordia, Krishnakant P Adhiya,
“Grouping Web Access Sequences Using
Sequence Alignment Method”, IJCSE, Vol. 2 No.
3 Jun-Jul 2011
[8] Chhavi Rana, “A Study of Web Usage Mining
Research Tools”, Int. J. Advanced Networking and
Applications,Volume:03 Issue:06 Pages:1422-1429
(2012)
[9] T. Vijaya kumar & H. S. Guruprasad, “Clustering
Of Web Usage Data Using Fuzzy Tolerance
Rough Set Similarity and Table Filling Algorithm”,
IJCSEITR, Vol. 3, Issue 2, Jun 2013, 143-152
[10]Norwati Mustapha, Manijeh Jalali, Abolghasem
Bozorgniya, Mehrdad Jalali, “Navigation Patterns
Mining Approach based on Expectation
Maximization Algorithm”, World Academy of
Science, Engineering and Technology Vol:3 2009-
02-26
[11]V.Sujatha, Punithavalla, , “Improved User
Navigation Pattern Prediction Technique From
Web Log Data”, International Conference on
Communication Technology and System Design
2011
[12]K. Devipriyaa and Dr. B.Kalpana, “Users'
Navigation Pattern Discovery using Ant Based
Clustering and LCS Classification”, JGRCS,
Volume 1, No. 1, August 2010
[13]Supreet Dhillon, Kamaljit Kaur , “ Comparative
Study of Classification Algorithms for Web Usage
Mining”, IJARCSSE, Volume 4, Issue 7, July 2014
[14]T.Vijaya Kumar, Dr. H.S.Guruprasad2, “Clustering
Of Web Usage Data Using Chameleon
Algorithm”, IJIRCCE, Vol. 2, Issue 6, June 2014
[15]Naresh Barsagade, “Web Usage Mining and
Pattern Discovery: A Survey Paper”
[16]Nanhay Singh1, Achin Jain1, Ram Shringar Raw1.
“Comparison Analysis Of Web Usage Mining
Using Pattern Recognisation Techniques”,
International Journal of Data Mining & Knowledge
Management Process (IJDKP) Vol.3, No.4, July
2013
[17]Abdelhakim Herrouz, Chabane Khentout
Mahieddine Djoudi, “ Overview Of Web Content
Mining Tools”, The International Journal Of
Engineering And Science (IJES) ,Volume 2 ,Issue
6 ,2013
[18]Shanmuga Priya, S. Sakthivel, “An Implementation
Of Web Personalization Using Web Mining
Techniques”, IJCSMC, Vol. 2, Issue. 6, June 2013,
pg.145 – 150