Enhanced PL-WAP tree method for incremental mining of sequential patterns.

Sequential mining as web usage mining has been used in improving web site design, increasing volume of e-business and providing marketing decision support. This thesis proposes PL4UP and EPL4UP algorithms which use the PLWAP tree structure to incrementally update sequential patterns. PL4UP does not scan old DB except when previous small 1-itemsets become large in updated database during which time its scans only all transactions in the old database that contain any small itemsets. EPL4UP rebuilds the old PLWAP tree using only the list of previous small itemsets once rather than scanning the entire old database twice like original PLWAP. PL4UP and EPL4UP first update old frequent patterns on the small PLWAP tree built for only the incremented part of the database, then they compare new added patterns generated from the small tree with the old frequent patterns to reduce the number of patterns to be checked on the old PLWAP tree. (Abstract shortened by UMI.) Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .C47. Source: Masters Abstracts International, Volume: 42-03, page: 0959. Adviser: Christie Ezeife. Thesis (M.Sc.)--University of Windsor (Canada), 2003

Web Page Recommendation Using Domain Knowledge and Improved Frequent Sequential Pattern Mining Algorithm

Web page recommendation is the technique of web site customization to fulfil the needs of every particular user or group of users. The web has become largest world of knowledge. So it is more crucial task of the webmasters to manage the contents of the particular websites to gather the requirements of the web users. The web page recommendation systems most part based on the exploitation of the patterns of the site's visitors. Domain ontology’s provide shared and regular understanding of a particular domain. Existing system uses pre-order linked WAP-tree mining (PLWAP Mine) algorithm that helps web recommendation system to recommend the interested pages but it has some drawbacks, it require more execution time and memory. To overcome the drawbacks of existing system paper utilizes PREWAP algorithm. The PREWAP algorithm recommends the interested results to web user within less time and with less memory and improves the efficiency of web page recommendation system. In work, various models are presented; the first model is Web Usage Mining which uses the web logs. The second model also utilizes web logs to represent the domain knowledge, here the domain ontology is used to solve the new page problem. Likewise the prediction model, which is a network of domain terms, which is based on the frequently viewed web-pages and represents the integrated web usage. The recommendation results have been successfully verified based on the results which are acquired from a proposed and existing web usage mining (WUM) technique

Mining frequent sequential patterns in data streams using SSM-algorithm.

Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. In data stream applications, data arrive at high speed rates in a continuous flow. Data stream mining is an online process different from traditional mining. Traditional mining algorithms work on an entire static dataset in order to obtain results while data stream mining algorithms work with continuously arriving data streams. With rapid change in technology, there are many applications that take data as continuous streams. Examples include stock tickers, network traffic measurements, click stream data, data feeds from sensor networks, and telecom call records. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand. This thesis proposes SSM-Algorithm (sequential stream mining-algorithm) that delivers frequent sequential patterns in data streams. The concept of this work came from FP-Stream algorithm that delivers time sensitive frequent patterns. Proposed SSM-Algorithm outperforms FP-Stream algorithm by the use of a hash based and two efficient tree based data structures. All incoming streams are handled dynamically to improve memory usage. SSM-Algorithm maintains frequent sequences incrementally and delivers most current result on demand. The introduced algorithm can be deployed to analyze e-commerce data where the primary source of the data is click stream data. (Abstract shortened by UMI.)Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .M668. Source: Masters Abstracts International, Volume: 44-03, page: 1409. Thesis (M.Sc.)--University of Windsor (Canada), 2005

Mining of uncertain Web log sequences with access history probabilities

An uncertain data sequence is a sequence of data that exist with some level of doubt or probability. Each data item in the uncertain sequence is represented with a label and probability values, referred to as existential probability, ranging from 0 to 1. Existing algorithms are either unsuitable or inefficient for discovering frequent sequences in uncertain data. This thesis presents mining of uncertain Web sequences with a method that combines access history probabilities from several Web log sessions with features of the PLWAP web sequential miner. The method is Uncertain Position Coded Pre-order Linked Web Access Pattern (U-PLWAP) algorithm for mining frequent sequential patterns in uncertain web logs. While PLWAP only considers a session of weblogs, U-PLWAP takes more sessions of weblogs from which existential probabilities are generated. Experiments show that U-PLWAP is at least 100% faster than U-apriori, and 33% faster than UF-growth. The UF-growth algorithm also fails to take into consideration the order of the items, thereby making U-PLWAP a richer algorithm in terms of the information its result contains

Mining High Utility Sequential Patterns from Uncertain Web Access Sequences using the PL-WAP

In general, the web access patterns are retrieved from the web access sequence databases using various sequential pattern algorithms such as GSP, WAP, and PLWAP tree. However, these algorithms do not consider sequential data with quantity (internal utility) (e.g., the amount of the time spent by the user on a web page) and quality (external utility) (e.g., the rating of a web page in a website) information. These algorithms also do not work on uncertain sequential items (e.g., purchased products) having probability (0, 1). Factoring in the utility and uncertainty of each sequence item provides more product information that can be beneficial in mining profitable patterns from company’s websites. For example, a customer can purchase a bottle of ink more frequently than a printer but the purchase of a single printer can yield more profit to the business owner than the purchase of multiple bottles of ink. Most existing traditional uncertain sequential pattern algorithms such as U-Apriori, UF-Growth, and U-PLWAP do not include the utility measures. In U-PLWAP, the web sequences are derived from web log data without including the time spent by the user and the web pages are not associated with any rating. By considering these two utilities, sometimes the items with lower existential probability can be more profitable to the website owner. In utility based traditional algorithms, the only algorithm related to both uncertain and high utility is the PHUI-UP algorithm which considers the probability and utility as different entities and the retrieved patterns are not dependent with both due to two different thresholds, and it does not mine uncertain web access database sequences. This thesis proposes the algorithm HUU-PLWAP miner for mining uncertain sequential patterns with internal and external utility information using PLWAP tree approach that cut down on several database scans of level-wise approaches. HUU-PLWAP uses uncertain internal utility values (derived from sequence uncertainty model) and the constant external utility values (predefined) to retrieve the high utility sequential patterns from uncertain web access sequence databases with the help of U-PLWAP methodology. Experiments show that HUU-PLWAP is at least 95% faster than U-PLWAP, and 75% faster than the PHUI-UP algorithm

Mining very long sequences with PLWAPLong algorithms

Sequential pattern mining is the process of finding inter-transaction frequent sequential patterns from a sequential database, where records consist of ordered sets of events (or items), by applying data mining techniques on such sequential databases. Discovering sequential patterns in web server logs is an example application of sequential mining, which is useful for predicting visiting patterns of web users for such purposes as targeted advertisements. Position Coded Pre-order Linked Web Access Pattern (PLWAP) mining algorithm is one of the existing efficient web sequential pattern mining algorithms, which stores the frequently stored sequences of the entire sequential database in a compressed tree form with position coded nodes. However, for very long sequences exceeding thirty two nodes, the number of bits an integer position code can hold, the PLWAP algorithm\u27s performance begins to degrade because it employs linked lists to store conjunctions of long position codes and the linked list traversals slow down the algorithm both during tree construction and mining. PLWAP algorithm also uses each and every node in the frequent 1-item event queue to test for that event inclusion in the suffix tree root set during mining. This is a very expensive operation since except for one node all other nodes that are its ancestors and descendents are not included in the root set. This thesis proposes two new algorithms, i.e. PLWAPLong1 and PLWAPLong2. Both of these new algorithms use a new position code numbering scheme where each node is assigned two numeric variables (startPosition, endPosition) instead of one. Using this scheme we can determine the ancestor node in O (1) operation by comparing the startPosition and endPosition of two nodes. PLWAPLong1 algorithm also proposes transforming the linked list based tree to an equivalent array representation and using binary search to find the immediate descendant in a suffix tree. PLWAPLong2 uses existing linked list based tree. Both PLWAPLong1 and PLWAPLong2 algorithms introduce a new technique called Last Descendant to eliminate the unwanted nodes from ancestor/descendent test when creating the suffix tree root set. Keywords: Data mining, Web Mining, Association Rule Mining, Long Sequences, PLWAP Minin

Frequent Pattern mining with closeness Considerations: Current State of the art

Due to rising importance in frequent pattern mining in the field of data mining research, tremendous progress has been observed in fields ranging from frequent itemset mining in transaction databases to numerous research frontiers. An elaborative note on current condition in frequent pattern mining and potential research directions is discussed in this article. It2019;s a strong belief that with considerably increasing research in frequent pattern mining in data analysis, it will provide a strong foundation for data mining methodologies and its applications which might prove a milestone in data mining applications in mere future

Web-Page Recommendation Based on Web Usage and Domain Knowledge

© 1989-2012 IEEE. Web-page recommendation plays an important role in intelligent Web systems. Useful knowledge discovery from Web usage data and satisfactory knowledge representation for effective Web-page recommendations are crucial and challenging. This paper proposes a novel method to efficiently provide better Web-page recommendation through semantic-enhancement by integrating the domain and Web usage knowledge of a website. Two new models are proposed to represent the domain knowledge. The first model uses an ontology to represent the domain knowledge. The second model uses one automatically generated semantic network to represent domain terms, Web-pages, and the relations between them. Another new model, the conceptual prediction model, is proposed to automatically generate a semantic network of the semantic Web usage knowledge, which is the integration of domain knowledge and Web usage knowledge. A number of effective queries have been developed to query about these knowledge bases. Based on these queries, a set of recommendation strategies have been proposed to generate Web-page candidates. The recommendation results have been compared with the results obtained from an advanced existing Web Usage Mining (WUM) method. The experimental results demonstrate that the proposed method produces significantly higher performance than the WUM method