There is a great need of developing efficient content based image retrieval systems because of the availability of large image databases. A new image retrieval system CTDCIRS (color-texture and dominant color based image retrieval system) to retrieve the images using three features called dynamic dominant color (DDC), Motif co-occurrence matrix (MCM) and difference between pixels of scan pattern (DBPSP) is proposed. Initially the image is divided into eight coarse partitions using the fast color quantization algorithm and the eight dominant colors are obtained from eight partitions. Next the texture of the image is represented by the MCM and DBPSP. MCM is derived using a motif transformed image. MCM is similar to color co-occurrence matrix (CCM). MCM is the conventional pattern cooccurrence matrix that calculates the probability of the occurrence of same pixel color between each pixel and its adjacent ones in each image, and this probability is considered as the attribute of the image.MCM captures third order image statistics in the local neighborhood which describes the direction of textures but not the complexity of the textures. That is why the DBPSP is also considered as one of the texture features. The three features Dominant color, MCM and DBPSP are integrated to facilitate the image retrieval system. Experimental results show that the proposed image retrieval is more efficient in retrieving the user-interested images.

The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The amount of information grows billions of databases. We need to search the information will specialize tools known generically search engine. There are many of search engines available today, retrieving meaningful information is difficult. However to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper we present survey on the search engine generations and the role of search engines in intelligent web and semantic search technologies.

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Clustering is one of the data mining techniques that have been around to discover business intelligence by

Nowadays people are interested in using digital images. So the size of the image database is increasing enormously. Lot of interest is paid to find images in the database. There is a great need for developing an efficient technique for finding the images. In order to find an image, image has to be represented with certain features. Color and texture are two important visual features of an image. So, an efficient image retrieval technique which uses local color and texture features is proposed. An image is partitioned into sub-blocks of equal size as a first step. Color of each sub-block is extracted by quantifying the HSV color space into non-equal intervals and the color feature is represented by cumulative histogram. Texture of each sub-block is obtained by using gray level cooccurrence matrix. A one to one matching scheme is used to compare the query and target image. Euclidean distance is used in retrieving the similar images. The efficiency of the method is demonstrated with the results. General TermsAlgorithm, search, match.

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Data Stream Mining algorithms performs under constraints called space used and time taken, which is due to the streaming property. The relaxation in these constraints is inversely proportional to the streaming speed of the data. Since the caching and mining the streaming-data is sensitive, here in this paper a scalable, memory efficient caching and frequent itemset mining model is devised. The proposed model is an incremental approach that builds single level multi node trees called bushes from each window of the streaming data; henceforth we refer this proposed algorithm as a Tree (bush) based Incremental Frequent Itemset Mining (TIFIM) over data streams.

Factor Analysis is a very useful linear algebra technique used for dimensionality reduction. It is also used for data compression and visualization of high dimensional datasets. This technique tries to identify from among a large set of variables, a reduced set of components which summarizes the original data. This is done by identifying groups of variables which have a strong inter correlation. The original variables are transformed into a smaller set of components which have a strong linear correlation. Using several data analysis techniques like Principal Components Analysis (PCA), Factor Analysis, cluster analysis may give insight into the patterns present in the data but may also give different results. The aim of this work is to study the use of Factor Analysis (FA) in capturing the cluster structures from transportation (HIS) data. It is proposed to compare the clustering obtained from original data from that of factor scores. Steps involved in preprocessing the transportation data are also illustrated.