Earticle

A new PCA algorithm is introduced, utilizing a rough cluster-based granulation scheme for segmentation of multivariate time series and process monitoring purposes. This granulated cluster-based algorithm can be used for segmentation of multivariate time series and initialization of other partitioning clustering methods that need to have good initialization parameters. The proposed algorithm is suitable for mining data sets, which are large both in dimension and size, in case generation. It utilizes Principal Component Analysis (PCA) specification and an innovative granular computing method for detection of changes in the hidden structure of multivariate time series data in a bottom up cluster merging manner. Rough set theory is used for feature extraction and solving superfluous attributes issue. The algorithm has been tested on an artificial case study. The resulting performances show the successful and promising capabilities of the proposed algorithm.

This paper presents about implementation of ontology and semantic approaches for accessing data from heterogeneous database. The purpose of this implementation is to retrieve relevant data and give relevant information to web users. The process of ontology and semantic are related. Two major processes in ontology are semantic mapping and extracting wrapper ontology. The main process for semantic web is to manipulate data into meaningful information. In this research, simple application was develop by using JAVA technology. JAVA technology was chosen because this technology have Jena library. This library is provide API and support SPARQL. After that, several experiments were done, and the results indicate implementation of ontology and semantic web approaches able to show relevant information to web users.

Discovery of association rules is an important problem in KDD process. In this paper we propose a new algorithm for fast frequent itemset mining, which scan the transaction database only once. All the frequent itemsets can be efficiently extracted in a single database pass. To attempt this objective, we define a new compact data structure, called ST-Tree (Signature Transaction Tree), and a new mining algorithm ST-Mine to extract frequent itemsets.

Mining of top-k items is much useful to a user than mining transactions for minimum support threshold. User may only provide expected minimum support after careful scanning of transaction. Still experience and expertise would be required. However user can much more easily project expected number of items to be included as per requirements. For this purpose, some approaches have been suggested but they rely on FP Tree modification. We have implemented another efficient technique for mining frequent itemsets from web logs. This technique is termed as WRDSP for Web Access Pattern Relative Dot Sequence Path. In this paper, we demonstrate this technique for finding frequent itemsets in case of transactions naming it as RDSP. After that we show, how this technique may be suitably modified for mining top-k itemsets. This technique scores over existing efficient techniques, which had been used in recent times. In this technique, each transaction updates the existing graph created by previous transactions, modifying the RDSP value associated with the link. The unique feature of the created RDSP graph is that it contains nodes equal to total number of items only. This significantly reduces the processing time and memory space required for ARM. The technique works optimally for small and moderate size database. Large databases give rise to enhanced RDSP, which are cumbersome in updating. Still, saving in number of access of database and efficient handling of generated RDSP graph achieved by the proposed technique make it a strong candidate for determining top-k itemsets.