Web Data Mining

Increasing progress in numerous research fields and information technologies, led to an increase in the publication of research papers. Therefore, researchers take a lot of time to find interesting research papers that are close to their field of specialization. Consequently, in this paper we have proposed documents classification approach that can cluster the text documents of research papers into the meaningful categories in which contain a similar scientific field. Our presented approach based on essential focus and scopes of the target categories, where each of these categories includes many topics. Accordingly, we extract word tokens from these topics that relate to a specific category, separately. The frequency of word tokens in documents impacts on weight of document that calculated by using a numerical statistic of term frequency-inverse document frequency (TF-IDF). The proposed approach uses title, abstract, and keywords of the paper, in addition to the categories topics to perform the classification process. Subsequently, documents are classified and clustered into the primary categories based on the highest measure of cosine similarity between category weight and documents weights.

Modern e-commerce applications require data
schemas that are constantly evolving and sparsely populated.
The Internet, e-commerce and e-business definitely hold an
important key to every organization’s future. Resting on the
innovation side of analytics, it’d be easy to lose yourself and
your business audience. Organizations can benefit more by
understanding customer’s interests and recommending the
right products to the right customer for their subsequent
purchases by identifying patterns in their earlier purchases.
This paper introduces a web data mining solution to ebusinesses
to discover hidden patterns and business strategies
from their customer demographic and web data.

This paper introduces the concept of product identity-clustering based on new similarity metrics and new performance metrics for web-crawled products. Product identity-clustering is defined here as the clustering of identical products, e.g., for price comparison purposes. Products blindly crawled over web sources, e.g., online marketplaces, have different description formats, where the features describing the same products differ in both number and representation formats. This problem causes imperfect feature vectors, where the vectors are considered to be not uniform in length and structure, with the features of various data types (numeric, categorical), and unknown vector structures. Furthermore, the product information usually contains redundant, missing, or faulty data, which are regarded as noise here. Product identity-clustering becomes a challenge when the vectors’ metadata are previously unknown and the imperfect nature of the feature vectors is considered with the occurrence of noise. In this paper, the product identity-clustering concept is introduced as a new mining metric in e-commerce. Then novel similarity metrics are introduced to improve the product identity-clustering performance of legacy metrics. Finally, novel performance metrics are proposed to measure the performance of the identity-clustering algorithms. Using these metrics, a comparison of the legacy-based similarity metrics (Euclidian, cosine, etc.) and the proposed similarity metrics is given. The results show that legacy metrics are not successful in discriminating identical web-crawled products and the proposed metrics enable better achievement in the product identity-clustering problem.

Key words: Product clustering, similarity metrics, identity clustering, performance metrics, web mining

Aggression in online social networks has been studied up to now, mostly with several machine learning methods which detect such behavior in a static context. However, the way aggression diffuses in the network has received little attention as it embeds modeling challenges. In fact, modeling how aggression propagates from one user to another, is an important research topic since it can enable effective aggression monitoring, especially in media platforms which up to now apply simplistic user blocking techniques. In this paper, we focus on how to model aggression propagation on Twitter, since it is a popular microblogging platform at which aggression had several onsets. We propose various methods building on two well-known diffusion models, Independent Cascade (IC) and Linear Threshold (LT), to study the aggression evolution in the social network. We experimentally investigate how well each method can model aggression propagation using real Twitter data, while varying parameters, such...

In recent years, the emergence of WWW (World Wide Web) led to the accumulation of huge amount of information and data. Hence the web is found to consist of unstructured and structured information that impacts the day to day life of the society. Because of such availability of huge information, utilization of the required information becomes more challenging. This paper provided a comprehensive survey on the current situation and recent trends on web content mining (WCM) and its applications thereby contributing to the enhancement of the upcoming research in WCM. The paper focused mainly on the mining and retrieval techniques, various WCM approaches, challenges and process of information retrieval and information extraction. The paper describes the four major tasks of web content mining that is information retrieval, information extraction, generalization and validation in detail. WCM concentrates on orchestrating, sorting, classifying, collecting, congregating of web data and provide the improved data which can be easily accessed by the users. Web content mining tools were needed to scan text, images and HTML documents and provide results to the search engine. It guides the search engine to provide better productive results for every search based on their importance. The paper also analysed different web content mining tools for the extraction of relevant information from the corresponding web page.

International Journal of Web & Semantic Technology (IJWesT) is a quarterly open access peer-reviewed journal that provides excellent international forum for sharing knowledge and results in theory, methodology and applications of web & semantic technology. The growth of the World-Wide Web today is simply phenomenal. It continues to grow rapidly and new technologies, applications are being developed to support end users modern life. Semantic Technologies are designed to extend the capabilities of information on the Web and enterprise databases to be networked in meaningful ways. Semantic web is emerging as a core discipline in the field of Computer Science & Engineering from distributed computing, web engineering, databases, social networks, Multimedia, information systems, artificial intelligence, natural language processing, soft computing, and human-computer interaction. The adoption of standards like XML, Resource Description Framework and Web Ontology Language serve as foundation technologies to advancing the adoption of semantic technologies.

Web is a wide, various and dynamic environment in which different users publish their documents. Web-mining is one of data mining applications in which web patterns are explored. Studies on web mining can be categorized into three classes: application mining, content mining and structure mining. Today, internet has found an increasing significance. Search engines are considered as an important tool to respond users' interactions. Among algorithms which is used to find pages desired by users is page rank algorithm which ranks pages based on users' interests. However, as being the most widely used algorithm by search engines including Google, this algorithm has proved its eligibility compared to similar algorithm, but considering growth speed of Internet and increase in using this technology, improving performance of this algorithm is considered as one of the web mining necessities. Current study emphasizes on Ant Colony algorithm and marks most visited links based on higher amount of pheromone. Results of the proposed algorithm indicate high accuracy of this method compared to previous methods. Ant Colony Algorithm as one of the swarm intelligence algorithms inspired by social behavior of ants can be effective in modeling social behavior of web users. In addition, application mining and structure mining techniques can be used simultaneously to improve page ranking performance.

Internet has become one of the most used tools for job search. Many websites identify millions of job vacancies; then these large datasets require practical technologies and analytical methods for extraction and data cleansing process. The aim of the paper is to build up a professional profile (Business analyst) obtained by extracting data from job vacancies published into the main vertical search engine (Indeed.com), via software (Python and R), using data mining techniques, for reducing mismatch in the market labour. The professional profile obtained has been compared with the one in the Information System on Occupations Istat-Inapp.

A distributed frameworks cluster is a gathering of machines that are for all intents and purposes or topographically isolated and that cooperate to give a similar services or application to customers in web application. It is conceivable that huge numbers of the services you keep running in your system today are a piece of a distributed system cluster. Cluster is a vital information mining procedure which expects to isolate the information objects into important gatherings called as groups. It is the way toward gathering objects into bunches to such an extent that items from a similar group are comparable and objects from various groups is unique. In information mining, information bunching has been examined for long time utilizing diverse calculations and ordinary patterns are proposed for better results around tailed data. The fuzzy semantic strategy is look at to group the overwhelming followed information by utilizing some technique for remove. An appraisal thinks about is introduced in view of time and exactness. In this method proposed here is evaluated to other relational clustering schemes using various propinquity matrices as input. Simulations demonstrate the scheme to be very effectual.

These days, billions of Web pages are created with HTML or other markup languages. They only have a few uniform structures and contain various authoring styles compared to traditional text-based documents. However, users usually focus on a particular section of the page that presents the most relevant information to their interest. Therefore, Web documents classification needs to group and filter the pages based on their contents and relevant information. Many researches on Web mining report on mining Web structure and extracting information from Web contents. However, they have focused on detecting tables that convey specific data, not the tables that are used as a mechanism for structuring the layout of Web pages. Case modeling of tables can be constructed based on structure abstraction. Furthermore, Ripple Down Rules (RDR) is used to implement knowledge organization and construction, because it supports a simple rule maintenance based on case and local validation.

The present paper proposes a theoretical approach to a language use that is considered exceptional to the rule. Specifically, under the title ‘category invasion’, this paper investigates a phenomenon in which neighboring categories with mutually distinct prototype centers deeply invade each other’s referential territory including the prototypical core. It attempts a theoretical description of the phenomenon by analyzing the critical notions of cognitive domain and dimension and explores its linguistic mechanism. Then, a carefully devised test using Web data detects an occurrence of invasion in the language of the Web. The study aims to show that invasion is a permanent and legitimate feature of language, to be treated on a par with such established notions as synonymy, overlap, and intersection rather than be dismissed as a simple result of bad use.

Abstract To gain the competitive advantage in today's age of technology, growing data and to bear the competitive pressure, making strong decisions according to customer's need and market trend has become very important. With huge amount of data on internet, web data ...

The explosive growth of the web has drastically changed the way in which information is managed and accessed. The large-scale of web data sources and the wide availability of services over the internet have increased the need for effective web data mining techniques and mechanisms . A sophisticated method to organize the layout of the information and assist user navigation

Web is a collection of interrelated 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, E-government, 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.

The cost of acquiring training data instances for induction of data mining models is one of the main concerns in real-world problems. The web is a comprehensive source for many types of data which can be used for data mining tasks. But the distributed and dynamic nature of web dictates the use of solutions which can handle these characteristics. In this paper, we introduce an automatic method for topical data acquisition from the web. We propose a new type of topical crawlers that use a hybrid link context extraction method for topical crawling to acquire on-topic web pages with minimum bandwidth usage and with the lowest cost. The new link context extraction method which is called Block Text Window (BTW), combines a text window method with a block-based method and overcomes challenges of each of these methods using the advantages of the other one. Experimental results show the predominance of BTW in comparison with state of the art automatic topical web data acquisition methods based on standard metrics.

Academic industries used to collect feedback from the students on the main aspects of course such as preparations, contents, delivery methods, punctual, skills, appreciation, and learning experience. The feedback is collected in terms of both qualitative and quantitative scores. Recent approaches for feedback mining use manual methods and it focus mostly on the quantitative comments. So the evaluation cannot be made through deeper analysis. In this paper, we develop a student feedback mining system (SFMS) which applies text analytics and sentiment analysis approach to provide instructors a quantified and deeper analysis of the qualitative feedback from students that will improve the students learning experience. We have collected feedback from the students and then text processing is done to clean the data. Features or topics are extracted from the pre-processed document. Feedback comments about each topic are collected and made as a cluster. Classify the comments using sentiment classifier and apply the visualization techniques to represent the views of students. This proposed system is an efficient approach for providing qualitative feedback for the instructor that enriches the students learning.

Deep web contents are accessed by queries submitted to web databases and the returned data records are enwrapped in dynamically generated web pages (they will be called deep Web pages). Extracting structured data from deep web pages is a challenging problem due to the underlying intricate structures of such pages. As the popular two-dimensional media, the contents on web pages are always displayed regularly for users to browse. This motivates us to seek a different way for deep web data extraction to overcome the limitations of previous works by utilizing some interesting common visual features on the deep web pages. In this paper, an agent based authentication mechanism is proposed which uses an agent program runs on the server to authenticate the user. A novel vision-based approach that is web page programming language-independent is also proposed. This approach primarily utilizes the visual features on the deep web pages to implement deep web data extraction, including data record extraction and data item extraction. Our experiments on a large set of web databases show that the proposed vision-based approach is highly effective for deep web data extraction and provides security for the registered user. Keywords— Authentication, vision based approach, web data, agent program, web page.

Nowadays YouTube becoming most popular video sharing website, and is established in 2005. The YouTube official website is providing different categories videos including Science and Technology, Films and Animation, News and politics, Movies, Comedy, Sports, Music etc. Each video hosted in website such as YouTube have its own identity and features. The identity and features of each video can be described by web video metadata objects such as- URL of each video, category, length of the video, rating information, view counts, comment information, key words etc. Using extracted web video metadata objects, we present an in-depth and systematic clustering study on the metadata objects of YouTube videos using Expectation Maximization (EM) and Density Based (DB) clustering approach. Distinct web video metadata object clusters are formed based on different category of web videos. The resultant clusters are analyzed in depth as a step in the KDD process.