Recent emerging growth of data created so manychallenges in data mining. Data mining is the process of extracting valid, previously known & comprehensive datasets for the future decision making. As the improved technology by World Wide Web the streaming data come into picture with its challenges. The data which change with time & update its value is known as streaming data. As the most of the data is streaming in nature, there are so many challenges need to face in the sense of security perspective. Intrusion Detection System (IDS) works in the supposition of detecting the intruders to protect the respective system. The research in data stream mining & Intrusion detection system gained high attraction due to the importance of system’s safety measure. Algorithms, systems & frameworks that address security challenges have been developed over the past years. In this paper, we present the mechanism to improve the efficiency of the IDS using streaming data mining technique. We apply four selected stream data classification algorithms on NSL-KDD datasets and compare their results. Based on the comparative analysis of their results best method is found out for efficiency improvement of IDS.

This paper proposes a platform for extraction and summarizing of opinions expressed by users in tourism related onlineplatforms. Extracting opinions from user generated reviews, regarding aspects specific to hotel services, are useful both to clients looking for accommodation, and also hotels trying to improve their services. The proposed system extracts hotel reviews from internet and classifies them, using an opinion mining technique. Platform is evaluated using a manually pre-classified dataset of user reviews. In the paper the efficiency of algorithms are analyzed using text mining domain specific measures, and are proposed methods for improving the results.

In this paper, the main area of concenhutionwos to optimize the rules generated by Association Rule Mining (apriori metho4, using Genetic Algorithms. In general the rule generated by Association Rule Mining technique do not consider the negative occurrences of attributes in them, but by using Genetic Algorithms (GAS) over these rules the system can predict the rules which contains negative attributes. The main motivation for using GAS in the discovery of high-levelprediction rules is that they pe$orm a global search and cope better with ottribute interaction than the gree4 rule induction algorithms open used in data mining. The improvements opplied in GAS ore definitely going to kelp the rule bmedsystems used for classification as described in results and conclusions.

People generally access the information over the internet withthe help of search engines. Search engines are the programs which find the specific pages for users according to their query. Web page ranking is the most important factor on internet for search engines. Web page ranking is a technique that ranks the web pages according to their different qualities and parameters for search engines. There are various web search engines are available on internet some of them are Google, Yahoo, and Bing etc. In this paper, we present a new web ranking system by using Semantic Similarity and HITS algorithm along with AI technique. These techniques work together to rank a web page from a number of web pages on the internet.

Association rule mining is the task of data mining ,which generates rules based on the relationships between the set of items purchased.We propose an algorithm, namely utility pattern rare itemset (UPRI) , for mining high utility rare itemsets with a set of strategies. The information of high utility itemsets is maintained in a tree-based data structure namely utility pattern rare tree (UPR-Tree).Utility mining aims to discover itemsets with high utilities by considering profit, quantity, cost or other user preferences. In retail business high consideration should be given to utility of item in a transaction, since items having low selling frequencies may have high profits. Rare itemsets provide useful information in different decision making domains. In this paper, UPRI algorithm has been proposed to generate high utility rare itemsets .These itemsets occur infrequently in a transactional database but may generate huge profits for a business.

In many online shopping applications, such as Amazon and eBay, traditionalAssociation Rule (AR) mining has limitations as it only deals with the items that are sold but ignores the items that are almost sold (for example, those items that are put into the basket but not checked out). We say that those almostsold items carry hesitation information, since customers are hesitating to buy them. The hesitation information of items is valuable knowledge for the design of good selling strategies. However, there is no conceptual model that is able to capture different statuses of hesitation information. Herein, we apply and extend vague set theory in the context of AR mining. We define the concepts of attractiveness and hesitation of an item, which represent the overall information of a customer’s intent on an item. Based on the two concepts, we propose the notion of Vague Association Rules (VARs).We devise an efficient algorithm to mine the VARs. Our experiments show that our algorithm is efficient and the VARs capture more specific and richer information than do the traditional ARs.

E-commerce organizations are growing exponentially with time in terms of both business and data. Many organizations rely on these websites to attract new customers and retain the existing ones. In order to achieve this goal web log files can be used that records customer's access patterns. Using traditional web usage mining techniques in an enhanced manner valuable patterns and hidden knowledge can be discovered. This paper focuses on providing real time dynamic recommendation to all the visitors of the website irrespective of been registered or unregistered. Action based rational recommendation technique is proposed that makes use of lexical patterns to generate item recommendation. Effectiveness of the proposed system is evaluated by collecting real time E commerce data and comparing the system with user based and product based techniques. Results prove that the proposed system yield good quality accuracy and minimizes limitations of traditional recommendation system.

The image descriptors based on multi-features fusion have better performance than that based on simple feature in content-based image retrieval (CBIR). However, these methods still have some limitations: 1) the methods that define directly texture in color space put more emphasis on color than texture feature; 2) traditional descriptors based on histogram statistics disregard the spatial correlation between structure elements; 3) the descriptors based on structure element correlation (SEC) disregard the occurring probability of structure elements. To solve these problems, we propose a novel image descriptor, called Global Correlation Descriptor (GCD), to extract color and texture feature respectively so that these features have the same effect in CBIR. In addition, we propose Global Correlation Vector (GCV) and Directional Global Correlation Vector (DGCV) which can integrate the advantages of histogram statistics and SEC to characterize color and texture features respectively. Experimental results demonstrate that GCD is more robust and discriminative than other image descriptors in CBIR

A novel content-based image retrieval (CBIR) schema with wavelet and color features followed by ant colony optimization (ACO) feature selection has been proposed in this paper. A new feature extraction schema including texture features from wavelet transformation and color features in RGB and HSV domain is proposed as representative feature vector for images in database. Also, appropriate similarity measure for each feature is presented. Retrieving results are so sensitive to image features used in content-based image retrieval. We address this problem with selection of most relevant features among complete feature set by ant colony optimization based feature selection. To evaluate the performance of our proposed CBIR schema, it has been compared with older proposed systems, results show that the precision and recall of our proposed schema are higher than older ones for the majority of image categories

In this letter, we propose a new adaptive weighted mean filter (AWMF) for detecting and removing high level of salt-and-pepper noise. For each pixel, we firstly determine the adaptive window size by continuously enlarging the window size until the maximum and minimum values of two successive windows are equal respectively. Then the current pixel is regarded as noise candidate if it is equal to the maximum or minimum values, otherwise, it is regarded as noise-free pixel. Finally, the noise candidate is replaced by the weighted mean of the current window, while the noise-free pixel is left unchanged. Experiments and comparisons demonstrate that our proposed filter has very low detection error rate and high restoration quality especially for high-level noise.