Web Access Patterns Research Articles

Data Mining Tools for Generate Item Set : Critical Review

Most algorithms used to identify large itemsets can be classified as either sequential or parallel. In most cases, it is assumed that the itemsets are identified and stored in lexicographic order (based on item name). This ordering provides a logical manner in which itemsets can be generated and counted. This is the normal approach with sequential algorithms. On the other hand, parallel algorithms focus on how to parallelize the task of finding large itemsets. Mining Associations is one of the techniques involved in the process mentioned in chapter 1 and among the data mining problems it might be the most studied ones. Discovering association rules is at the heart of data mining. Mining for association rules between items in large database of sales transactions has been recognized as an important area of database research. These rules can be effectively used to uncover unknown relationships, producing results that can provide a basis for forecasting and decision making. Today, research work on association rules is motivated by an extensive range of application areas, such as banking, manufacturing, health care, and telecommunications. It is also used for building statistical thesaurus from the text databases, finding web access patterns from web log files, and also discovering associated images from huge sized image databases.

Web page prediction using adaptive deer hunting with chicken swarm optimization based neural network model

The world wide web acts as the dominant tool for data transmission due to access such as data retrieving and data transactions. The retrieval of data from the web is a complex procedure due to the large volume of web domain. The basic uses of the websites are described through web usage mining, which mines the weblog records to identify the pattern of accessing the web pages through the user. The web page prediction assists the web users in finding the plot and obtains the information as to their requirements. Several effective algorithms have been developed to mine association rules that make the predictive model more appropriate for web prediction. They can be commonly revised to ensure the changing feature of web access patterns. The Apriori algorithm involves extracting the recurrent itemset and interrelation rule that learns the relational data is commonly utilized for web page prediction. The Apriori algorithm remains the standard model for deriving the patterns and rules from the datasets in co-operative rule extraction. The Apriori algorithm thus generates large mines associated rules for web page prediction. Hence, to select the best rule, the proposed deer hunting rooster-based chicken swarm optimization algorithm is used by integrating the cockerel search agents’ dominating social search creatures’ hunting habits and their traits of looking for food. Further, the neural network (NN) is employed in this research for the prediction of web pages with minimum error. The trained NN is a technique of unsupervised learning that analyzes a dataset of input to produce the desired result, in which the effectiveness of the NN is enhanced by optimal tuning of weight by the adaptive deer hunting rooster-based chicken swarm optimization algorithm. The experimental analysis illustrates that the proposed adaptive deer hunting rooster-based chicken swarm optimization frameworks inherit lower error measures such as mean deviation = 139.89 and symmetric mean absolute percentage error[Formula: see text]0.45579 for the FIFA dataset. The proposed web page prediction models’ L2 norm and infinity norm are 58.017 and 14, respectively, for the MSNBC_SPMF dataset.

Mutual Information Pre-processing Based Broken-stick Linear Regression Technique for Web User Behaviour Pattern Mining

Web usage behaviour mining is a substantial research problem to be resolved as it identifies different user’s behaviour pattern by analysing web log files. But, accuracy of finding the usage behaviour of users frequently accessed web patterns was limited and also it requires more time. Mutual Information Pre-processing based Broken-Stick Linear Regression (MIP-BSLR) technique is proposed for refining the performance of web user behaviour pattern mining with higher accuracy. Initially, web log files from Apache web log dataset and NASA dataset are considered as input. Then, Mutual Information based Pre-processing (MI-P) method is applied to compute mutual dependence between the two web patterns. Based on the computed value, web access patterns which relevant are taken for further processing and irrelevant patterns are removed. After that, Broken-Stick Linear Regression analysis (BLRA) is performed in MIPBSLR for Web User Behaviour analysis. By applying the BLRA, the frequently visited web patterns are identified. With the identification of frequently visited web patterns, MIP-BSLR technique exactly predicts the usage behaviour of web users, and also increases the performance of web usage behaviour mining. Experimental evaluation of MIPBSLR method is conducted on factors such as pattern mining accuracy, false positives, time requirements and space requirements with respect to number of web patterns. Outcomes show that the proposed technique improves the pattern mining accuracy by 14%, and reduces the false positive rate by 52%, time requirement by 19% and space complexity by 21% using Apache web log dataset as compared to conventional methods. Similarly, the pattern mining accuracy of NASA dataset is increased by 16% with the reduction of false positive rate by 47%, time requirement by 20% and space complexity by 22% as compared to conventional methods.

Analyzing and Optimizing the Usability of Website Access

The world wide web (WWW) plays a significant role in information sharing and distribution. In web-based information access, the speed of information retrieval plays a critical role in shaping the web usability and determining the user satisfaction in accessing webpages. To deal with this problem, web caching is used. The problem with the present web caching system is that it is very hard to recognize webpages that are to be accessed and then to be cached. This is forced by the fact that there are broad categories of users and each one having their own preferences. Hence, it is decided to propose a novel approach for web access pattern generation by analyzing the web log file present in the proxy server. Further, it tries to propose a novel hybrid policy called popularity-aware modified least frequently used (PMLFU) that best suits for the current proxy-based web caching environment. It combines features such as frequency, recency, popularity, and user page count in decision-making policy. The performance of the proposed system is observed using real-time datasets, empirically using IRCACHE datasets.

A Parallel Approach for Web Session Identification to make Recommendation Efficient

A sequence of web pages visited by the clients over a particular timeframe is called a session. Web log mining is done to analyse the behaviour of the users, using the web access patterns. Sessions are identified as the significant part of the construction of the recommendation model. The novel part of the work makes use of backward moves made by the user, considering both the referrer url and the requested url extracted from the extended web log for session identification which is not taken into consideration in the existing heuristic-based approach. Two noteworthy issues in session identification are: 1) framing excessively numerous smaller length sessions; 2) taking longer time for identifying the sessions. In the proposed work, the length of the sessions are maximised using split and merge technique and the time taken for session identification is reduced using thread parallelisation. For efficient storage and retrieval of information the hash map data structure is used. The proposed work shows significant improvement in performance in terms of execution time, standard error, correlation coefficient and the objective value.

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Mining web access patterns with super-pattern constraint

We consider the problem of mining web access patterns with super-pattern constraint. This constraint requires that the sequential patterns in the sequence database must contain a particular set of patterns as sub-patterns. One common application of this constraint is web usage mining which mines the user access behavior on the web. In this paper, we introduce an efficient strategy for mining web access patterns with super-pattern constraint that requires only one database scan. Firstly, we present the MWAPC (M ining W eb A ccess P atterns based on super-pattern C onstraint) algorithm, in which each frequent pattern has to be checked if it contains at least one pattern from a user-defined set of patterns. Then we develop an effective algorithm, called EMWAPC that prunes the search space at the beginning of mining process and avoids checking the constraints one by one based on three proposed propositions. We have conducted the experiments on real web log databases. The experimental results show that the proposed algorithms outperform the previous methods.

Web Users Clustering Based on Fuzzy C-MEANS

The Web contributes greatly to our life in many fields such as education, entertainment, Internet banking, online shopping and software downloading. This has led to rapid growth in the number of Internet users, which resulting in an explosive increase in traffic or bottleneck over the Internet performance. This paper proposes a new approach to group users according to their Web access patterns. The proposed approach for grouping users is based on Fuzzy c-means technique, which allows web users to be assigned into more than one cluster or interest. Each web user has a degree of membership of belonging to each cluster. The experimental results showed that the web users were successfully clustered to similar groups very fast using Fuzzy-c-means. In addition, the Fuzzy-c-means performed well and became much better when the clusters number increased on two real Bo2 and NY datasets. The proposed intelligent web users clustering based on Fuzzy-c-means can be used for discovering users' interests in Web pages that can contribute in enhancing several approaches such as Web caching, Web pre-fetching and Web recommender systems that are recently used to improve the Web performance.

A Constraint Programming Approach for Web Log Mining

Mining web access patterns consists in extracting knowledge from server log files. This problem is represented as a sequential pattern mining problem (SPM) which allows to extract patterns which are sequences of accesses that occur frequently in the web log file. There are in the literature many efficient algorithms to solve SMP (e.g., GSP, SPADE, PrefixSpan, WAP-tree, LAPIN, PLWAP). Despite the effectiveness of these methods, they do not allow to express and to handle new constraints defined on patterns, new implementations are required. Recently, many approaches based on constraint programming (CP) was proposed to solve SPM in a declarative and generic way. Since no CP-based approach was applied for mining web access patterns, the authors introduce in this paper an efficient CP-based approach for solving the web log mining problem. They bring back the problem of web log mining to SPM within a CP environment which enables to handle various constraints. Experimental results on non-trivial web log mining problems show the effectiveness of the authors' CP-based mining approach.

Multidimensional Web Access Pattern Tree (MD-WAP Tree)

Mining frequent web access patterns from large data (web log) is one significant application of sequential pattern mining. Web access patterns are set of frequent sub sequences that are useful to know user behaviour in real time in order to make dynamic decisions. Techniques for extracting web access patterns from data available in two flavours: apriori based and non apriori based (tree based). It has been observed that extracting web access pattern with respect to multiple dimensions gives interesting results rather than considering one dimension. In this paper a very interesting data structure, multidimensional web access pattern tree (MD-WAP Tree) is presented that can discover web access pattern with respect to multiple dimensions known as multidimensional web access patterns.

An Integrated Approach to Forecast the Future Requests of User by Weblog Mining

With the wide availability of information over the internet and amount of its’ potential users’ makes it challenging for service provider to make such relevant information available to users in a fast and personalized manner nowadays, resulting in decrease in the web performance. One way to tackle with this challenging issue is to use prediction or recommendation technique, which can make users to discover future offerings. Web Usage Mining is a discipline, where the navigational access behavior of users’ over the web is tracked and scrutinized. By collecting and analyzing this behavior of user activities, websites owner can easily identify the web access patterns of its users’. The mining of user access logs could help to improve the performance of search engines, since users have a specific goal when searching for information. In this paper, an approach is proposed to get more accurate prediction results by using both bi-clustering and greedy search method. The predictions of users’ future access requests will be made by this can improve the accuracy of results and will helps in order to reduce the search time. General Terms Data Mining

WaPUPS: Web access pattern extraction under user-defined pattern scoring

Extracting patterns from web usage data helps to facilitate better web personalization and web structure readjustment. The classical frequency-based sequence mining techniques consider only the binary occurrences of web pages in sessions that result in the extraction of many patterns that are not informative for users. To handle this problem, utility-based mining technique has emerged, which assigns non-binary values, called utilities, to web pages and calculates pattern utilities accordingly. However, the utility of a pattern cannot always be determined from distinct web page utilities. For instance, the number of distinct users that traverse an extracted pattern or some demographic data about those users may affect the value of the extracted patterns. However, such information cannot be calculated directly from web page utilities. In this paper, we present a new approach based on a user-defined scoring mechanism so as to extract patterns from web log data. The proposed approach can limit the size of the search space; therefore it has the ability to extract patterns even for large and sparse datasets. The framework is hybrid in the sense that it combines clustering with a heuristic-based pattern extraction algorithm. Substantial experiments on real datasets show that the proposed solution effectively discovers patterns under user-defined evaluation.

Efficient Mining of Robust Closed Weighted Sequential Patterns Without Information Loss

Sequential pattern mining has become one of the most important topics in data mining. It has broad applications such as analyzing customer purchase data, Web access patterns, network traffic data, DNA sequencing, and so on. Previous studies have concentrated on reducing redundant patterns among the sequential patterns, and on finding meaningful patterns from huge datasets. In sequential pattern mining, closed sequential pattern mining and weighted sequential pattern mining are the two main approaches to perform mining tasks. This is because closed sequential pattern mining finds representative sequential patterns which show exactly the same knowledge as the complete set of frequent sequential patterns, and weight-based sequential pattern mining discovers important sequential patterns by considering the importance of each sequential pattern. In this paper, we study the problem of mining robust closed weighted sequential patterns by integrating two paradigms from large sequence databases. We first show that the joining order between the weight constraints and the closure property in sequential pattern mining leads to different sets of results. From our analysis of joining orders, we suggest robust closed weighted sequential pattern mining without information loss, and present how to discover representative important sequential patterns without information loss. Through performance tests, we show that our approach gives high performance in terms of efficiency, effectiveness, memory usage, and scalability.

Algorithm for Mining Web Access Patterns Based on User Access Sequences

Through analysis of user access sequences, this research studies the browsing behaviors of web users and pro- poses a user access pattern mining algorithm based on the maximal forward reference sequence to determine frequent ac- cess paths. Experiments have shown that this effective algorithm could obtain a satisfactory result. This algorithm is supe- rior to the traditional Full Scan algorithm in terms of execution time and performance.

Mining Web Access Patterns using Root-set of Suffix Trees

With the rapidly growing uses of World Wide Web for various important and sensitive purposes it becomes a sensible necessity to find out the interesting web access patterns from the web access sequences tracked by users frequently. Web access sequential patterns can be used to achieve business intelligence for e-commerce sites and also can be used to analyze system performance. This paper proposes a more efficient web mining algorithm which mines all the sequential patterns from the web access sequences and totally eliminates the concept of linking between nodes. The algorithm uses the aggregate tree structure for mining and then mines from the tree using RST (Root-set of Suffix Trees) for same prefix items. The algorithm finds the frequent sequential patterns by recursively traversing the tree from root-nodes to child-nodes for the length-1 frequent items. The proposed approach doesn’t need to generate any projected tree; it needs only the root-set for each prefix that got in previous step. Experimental results show huge performance gain over the FOF and WAPtree mining techniques by considerably reducing the mining time.

An Efficient Algorithm for Sequence Generation in Data Mining

Data mining is the method or the activity of analyzing data from different perspectives and summarizing it into useful information. There are several major data mining techniques that have been developed and are used in the data mining projects which include association , classification , clustering , sequential patterns, prediction and decision tree . Among different tasks in data mining, sequential pattern mining is one of the most important tasks. Sequential pattern mining involves the mining of the subsequences that appear frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access patterns, seismologic data and weather observations. Various models and algorithms have been developed for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the efficiency of three sequence generation algorithms namely GSP, SPADE and PrefixSpan on a retail dataset by applying various performance factors. From the experimental results, it is observed that the PrefixSpan algorithm is more efficient than other two algorithms.

Web Access Patterns Mining for Individuals with Timing and Link Sequence

Web Access Patterns Mining for Individuals with Timing and Link Sequence

Web access pattern mining - a new method

An efficient web access pattern mining algorithm, FOL-mine is presented in this paper. The FOL-mine algorithm is based on the projected database of each frequent event and eliminates the need for construction of pattern tree. A data structure, first occurrence list (FOL), is introduced in the proposed algorithm for efficient handling of suffix building. Rebuilding of projection databases is completely eliminated in the new method. Experimental analysis of the algorithms reveals significant performance gain over other web access pattern mining algorithms.

Relevant Tiws Pattern Mining With Reduced Search Space

The sequential pattern mining finds all sequential patterns which occurs frequently for a given sequence database whose frequency is no less than the threshold. The weighted sequential pattern mining aims to find more interesting sequential patterns by considering the different significance of each data element in a sequence database. In the conventional weighted sequential pattern mining, pre-assigned weights of data elements are used to get the priorities which are derived from their quantitative information. Generally in sequential pattern mining, the generation order of data is considered to find sequential patterns. However, generation time and time-intervals between the data are also important in real world application domains. Therefore, time-intervals information of data elements can be helpful in finding more interesting sequential patterns. The proposed system presents a framework for finding time-interval weighted sequential (TiWS) patterns in a sequence database. In addition, the CloSpan algorithm is used for mining TiWS patterns in a sequence database which reduces the multiple database scans and the processing time. This is useful in application fields such as web access pattern analysis, customer purchase pattern analysis, DNA sequence analysis, etc.

Generation of Personalized Ontology Based on Consumer Emotion and Behavior Analysis

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.