International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 4, April - 2014 Comparative Study of Web Mining Algorithms Rachna Singh Bhullar Dr. Praveen Dhyani Computer Science Department, Banasthali University – Jaipur Campus, Jaipur Guru Nanak Dev University, Rajasthan, India. Amritsar, Punjab, India Abstract: Every second data is shared between web servers and databases. As a result, demand of most relevant and web users either in the form of uploading or downloading to or efficient data from the web databases and storage of data from the web sites. In both the cases web servers play a very important role of providing the most efficient and relevant data on the web databases is increasing exponentially. In such a from the web databases. To accomplish this task various web situation, task of retrieving the efficient data is extremely mining algorithms are used by web servers to satisfy the need of challenging. Web mining is the collection of those data web users. In this paper we are presenting an overview of web mining techniques which are best suited to perform such a mining algorithms and a comparative study among them. In the last section of this paper, we are suggesting some questions which challenging task.Web mining can be categorized into Web cannot be answered by any of the web content or web usage Usage Mining, Web Structure Mining, and Web Content mining algorithms which motivated us to implement the web Mining. These are described as follows:- structure mining algorithms. Web Usage Mining (WUM):- Keywords- Web Usage Mining, Web Content Mining, Web Structure Mining, Web Content Outlier Mining, Web Mining Algorithms. WUM consists of various web mining techniques to be applied on user access patterns and their profiles stored in 1. INTRODUCTION web server logs [1]. The results obtained from WUM are In today’s web scenario, web servers are behaving like the used by E-commerce agents to improve their interaction interface between the web databases and web users. The with the customers by analyzing their usage behavior. The job of web server is to upload or download the data from web usage mining process is explained as below [2]. IJERT IJERT Data (from server logs) (Sessions, IP address, user’s profiles) Preprocessing Pattern Discovery Pattern Analysis Knowledgeable Raw Usage data Preprocessed click Rules, Patterns and from stream data Statistics rules, pattern Web server logs and analysis Fig 1 Applications of Web Usage Mining:- Page recommendation is provided to web user only with WUM mainly involves Association rules, Clustering & the help of WUM techniques. Classification as sub-processes which are useful in: WUM is also helpful in fraud and intrusion detection. (a) Protection of next event. Dynamic recommendations for friends in social network (b) Discovery of web page visitors. sites or for purchases on E-commerce sites are one of the (c) Grouping of visitors on the basis of common mostly used applications of WUM. properties, interests, & common behavior. IJERTV3IS040350 www.ijert.org 219 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 4, April - 2014 WUM mining techniques can be applied to encourage Web Content Mining (WCM):- personalized marketing in E-commerce site. WCM is one of the specialized fields of web mining which Web usage Mining Algorithms:- deals with text, image, audios, videos, patterns, hyperlinks, Apriori algorithm is a common data mining technique for as the content of the web page. Further, mining techniques association based analysis. WUM phase includes data specifically applied on text are known as text mining. selection, data preprocessing, pattern discovery and Pattern analysis and spatial mining deals with the images analysis. Apriori algorithm is the part of pattern discovery and patterns. Mining techniques applied on video & audio sub function. signals fall into the category of Multimedia mining. If the An average linear time algorithm for WUM was suggested content of a web page contains hyperlinks to the related by Mark Levene and Jose Borges to show the linear web pages then WCM is categorized as Web Structure proportionality between the behavior of algorithm and no. Mining. The following figure summarizes the WCM as a of web pages accessed. whole: Web Content Mining Text Multimedia Spatial Web Structure Mining Text Mining Mining Mining (Accepts data (accepts audios, (accepts images (accepts hyperlinks As simple text videos signals & previous as the web or unstructured data) and images) drawn patterns) content) Figure2 In brief, WCM discovers new inferences or useful products, they make extensive use of content mining information. It is to be noted that simple text mining, algorithms as discussed ahead in this chapter. Multimedia Text Mining and Spatial Mining are three different fields, except the web page because the dataIJERT IJERTFraud Detection:- available through web is either unstructured or semi- ATM cards, Debit-Shopping cards, Credit cards etc. obtain structured. Generally, text and spatial mining deals with their authority after being checked through content mining structured text and images. Web content mining deals with algorithms embedded in their respective machines. the semi-structured or unstructured data. Communities:- Applications of Web Content Mining:- Social networking sites like Twitter, Facebook, Google and WCM is a vast research field but algorithms and methods LinkedIn all make use of WCM algorithms to give are applicable in various fields as explained below: authenticity to their users and their frauds. Ranking:- Web Structure Mining:- WWW is considered as a graph of connected web pages. WWW is considered as a web or directed graph having To retrieve the related ones is a challenging task; Search millions of nodes as the web pages. The hyperlinks to the engines make use of Ranking algorithms to rank the list of related pages are the edges in the directed graph. This related web pages on the basis of content to be searched graphical information is used by various Ranking and their popularity. algorithms like Page Rank, Weighted Page Rank and E-marketing:- Hypertext induced topic search algorithms to find the related web pages for a search query. The research on E-commerce is getting popular day by day so in order to WSM can be carried out in two ways. First one deal with improve the interaction between the customer and E- the structure of the web document, that is, structure of the company and to recommend related options for their HTML program to construct a web page. IJERTV3IS040350 www.ijert.org 220 International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 3 Issue 4, April - 2014 Second one considers the structure of hyperlinks in the the difference among the pages within the same category address part of a web page. In our research, we mainly [8]. focus on the hyperlinks and their structures. Hyperlinks in a web page, to another web page denote the relevancy between the two pages and are used to rank the importance DM =∑ [e (0.5+(0.5*f(t ,d )/maxfreq(d ))*log (N/K)]/e of the referred web page. In the statistics or numerical i i,j j j i i 10 i value analysis, values lying beyond some fixed range are where, known as outliers whereas in web mining, unauthentic, unauthorized and non related web pages are treated as F (tj,di)=frequency of term tjin di, document outliers. In traditional web outlier mining, web content mining algorithms are specially focused on the text in web Maxfreq (di)=maximum frequency of a word in the pages. There exist various algorithms like Signed approach document. for Web Content Outlier Mining [3] [4], Classic outlier N=total number of documents. detection from web clusters using dissimilarity measure [5], Chinese Web text outlier mining based on domain K=number of documents with term tj. knowledge[6] and Web content outlier-N-gram based algorithm [7]. ei=shows the words in the document that exist in the domain dictionary. Outline of work Section II provides the brief review of related work in web Finally, this paper identifies the outliers as the irrelevant content mining. Section III explains the proposed study. documents from the web clusters. The documents with Section IV provides the results of the study while in section minimum dissimilarity are the relevant ones. V future work is summarized. 2. RELATED WORK 3. WCOND-mine algorithm (Web Content Outliers-ngram) Web Content Outlier Mining Algorithms mine algorithm :- 1.Signed approach for Web Content Outlier Mining [4]. Malik Agyemang proposes this algorithm for mining web content outliers without using the domain dictionary. In G.Poonkuzhali, K.Thaigrajan, K,Sarukesi and G.V.Uma this paper, he is making use of N-gram technique to find proposed a system in which user provides a search query to IJERTthe outliers. The total no. of possible N-grams resulting search engine. As a result, ‘D’ web documents areIJERT from a string of length K is (K-N+1), where N=size of N- extracted from the web which in turn is preprocessed, that gram e.g. a string ‘sciences’ of length 8 has (8-5+1)=4, 5,- is the Stop word, Stun word and data like hyperlinks, grams (where K=8 and N=5) as ‘scien’, ‘cience’ and sound, images etc. are removed. The output set of ‘ences’. The N-gram technique is used to find the related documents containing white-space is represented in two- but different words. According to this N-gram technique, if dimensional format as (i, j), where ‘i’ represents the web two words having N-grams of length greater than half the page and ‘j’ represents word. The domain is related by length of string in common then these different words are taking into account such that all 1-letter word will be syntactically similar words.