An Efficient Mining Approach for Handling Web Access Sequences

The World Wide Web (WWW) becomes an important source for collecting, storing, and sharing the information. Based on the users query the traditional web page search approximately retrieves the related link and some of the search engines are Alta, Vista, Google, etc. The process of web mining defines to determine the unknown and useful information from web data. Web mining contains the two approaches such as data-based approach and process-based approach. Now a day the data-based approach is the widely used approach. It is used to extract the knowledge from web data in the form of hyper link, and web log data. In this study, the modern technique is presented for mining web access utility-based tree construction under Modified Genetic Algorithm (MGA). MGA tree are newly created to deploy the tree construction. In the web access sequences tree construction for the most part relies upon internal and external utility values. The performance of the proposed technique provides an efficient Web access sequences for both static and incremental data. Furthermore, this research work is helpful for both forward references and backward references of web access sequences

Efficient Incremental Breadth-Depth XML Event Mining

Many applications log a large amount of events continuously. Extracting interesting knowledge from logged events is an emerging active research area in data mining. In this context, we propose an approach for mining frequent events and association rules from logged events in XML format. This approach is composed of two-main phases: I) constructing a novel tree structure called Frequency XML-based Tree (FXT), which contains the frequency of events to be mined; II) querying the constructed FXT using XQuery to discover frequent itemsets and association rules. The FXT is constructed with a single-pass over logged data. We implement the proposed algorithm and study various performance issues. The performance study shows that the algorithm is efficient, for both constructing the FXT and discovering association rules

A review of associative classification mining

Associative classification mining is a promising approach in data mining that utilizes the association rule discovery techniques to construct classification systems, also known as associative classifiers. In the last few years, a number of associative classification algorithms have been proposed, i.e. CPAR, CMAR, MCAR, MMAC and others. These algorithms employ several different rule discovery, rule ranking, rule pruning, rule prediction and rule evaluation methods. This paper focuses on surveying and comparing the state-of-the-art associative classification techniques with regards to the above criteria. Finally, future directions in associative classification, such as incremental learning and mining low-quality data sets, are also highlighted in this paper

Anytime Hierarchical Clustering

We propose a new anytime hierarchical clustering method that iteratively transforms an arbitrary initial hierarchy on the configuration of measurements along a sequence of trees we prove for a fixed data set must terminate in a chain of nested partitions that satisfies a natural homogeneity requirement. Each recursive step re-edits the tree so as to improve a local measure of cluster homogeneity that is compatible with a number of commonly used (e.g., single, average, complete) linkage functions. As an alternative to the standard batch algorithms, we present numerical evidence to suggest that appropriate adaptations of this method can yield decentralized, scalable algorithms suitable for distributed/parallel computation of clustering hierarchies and online tracking of clustering trees applicable to large, dynamically changing databases and anomaly detection.Comment: 13 pages, 6 figures, 5 tables, in preparation for submission to a conferenc