Tansel Özyer

Intrusion detection by integrating boosting genetic fuzzy classifier and data mining criteria for rule pre-screening

The purpose of the work described in this paper is to provide an intelligent intrusion detection system (IIDS) that uses two of the most popular data mining tasks, namely classification and association rules mining together for predicting different behaviors in networked computers. To achieve this, we propose a method based on iterative rule learning using a fuzzy rule-based genetic classifier. Our approach is mainly composed of two phases. First, a large number of candidate rules are generated for each class using fuzzy association rules mining, and they are pre-screened using two rule evaluation criteria in order to reduce the fuzzy rule search space. Candidate rules obtained after pre-screening are used in genetic fuzzy classifier to generate rules for the classes specified in IIDS: namely Normal, PRB-probe, DOS-denial of service, U2R-user to root and R2L-remote to local. During the next stage, boosting genetic algorithm is employed for each class to find its fuzzy rules required to classify data each time a fuzzy rule is extracted and included in the system. Boosting mechanism evaluates the weight of each data item to help the rule extraction mechanism focus more on data having relatively more weight, i.e., uncovered less by the rules extracted until the current iteration. Each extracted fuzzy rule is assigned a weight. Weighted fuzzy rules in each class are aggregated to find the vote of each class label for each data item.

Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.

A Collaborative and Content Based Event Recommendation System Integrated with Data Collection Scrapers and Services at a Social Networking Site

There are many activities that people prefer/opt out attending and these events are announced for attracting people. An intelligent recommendation system can be used in a social networking site in order to recommend people according to content and collaboration assessment. This study is an effort to recommend events to users within a social networking site. It can be any networking environment. We have used social environment that has been designed as a facebook application. Our application has also been integrated with several web sites. System collects event data from several related web sites either by using web services or web scraping. It also permits users rating events they have attended or planned. Given the social network between people, system tries to recommend upcoming events to users. For this purpose a combination of content based and collaborative filtering has been used. We have also taken geographical location info and social concept of an event.

Parallel clustering of high dimensional data by integrating multi-objective genetic algorithm with divide and conquer

This paper applies divide and conquer approach in an iterative way to handle the clustering process. The target is a parallelized effective and efficient approach that produces the intended clustering result. We achieve scalability by first partitioning a large dataset into subsets of manageable sizes based on the specifications of the machine to be used in the clustering process; then cluster the partitions separately in parallel. The centroid of each obtained cluster is treated like the root of a tree with instances in its cluster as leaves. The partitioning and clustering process is iteratively applied on the centroids with the trees growing up until we get the final clustering; the outcome is a forest with one tree per cluster. Finally, a conquer process is performed to get the actual intended clustering, where each instance (leaf node) belongs to the final cluster represented by the root of its tree. We use multi-objective genetic algorithm combined with validity indices to decide on the number of classes. This approach fits well for interactive online clustering. It facilitates for incremental clustering because chunks of instances are clustered as stand alone sets, and then the results are merged with existing clusters. This is attractive and feasible because we consider the clustering of only centroids after the first clustering stage. The reported test results demonstrate the applicability and effectiveness of the proposed approach.

A mash-up application utilizing hybridized filtering techniques for recommending events at a social networking site

Event recommendation is one way of gathering people having same likes/dislikes. In today’s world, many mass amounts of events are organized at different locations and times. Generally, cliques of people are fans of some specific events. They attend together based on each other’s recommendation. Generally, there are many activities that people prefer/opt out attending and these events are announced for attracting relevant people. Rather than, peer-to-peer oracles of a local group of people, or sentiments of people from different sources, an intelligent recommendation system can be used at a social networking site in order to recommend people in collaborative and content basis within a social networking site. We have used an existing social environment (http://www.facebook.com) for deployment. Our application has also been integrated with several web sites for collecting information for assessment. Our system has been designed in modules so that it is open to new data sources either by using web services or web scraping. Currently, our application is yet an application that permits users rate events; they have attended or have beliefs on them. Given the social network between people, system tries to recommend upcoming events to users. For this purpose, we have exploited the fact that a similarity relationship between different events can exist in terms of both content and collaborative filtering. Geographical locations have an impact so; we have also taken geographical location information and social concept of an event. Eventually, our system integrates different sources in facebook (http://www.facebook.com) for doing recommendation between people in close relationship. We have performed experiments among a group of students. Experiments led us have promising results.

IEEE 802.11 WLAN based Real Time Indoor Positioning: Literature Survey and Experimental Investigations

Abstract Indoor positioning has emerged as a hot topic that gained gradual interest from both academia and industry. Accurate estimation is necessitated in a variety of location-based services such as healthcare, repository tracking, and security. Additional equipment for location sensing could be used for accurate estimation, but they are not widely used in general because those alternatives will cause specialization in brands and will be costly. Among all suggestions in literature including hardware and intense sophisticated computations, a versatile and low-cost location determination technology, which uses existing WLAN infrastructure of indoor environments, has been developed without incurring extra charge; this method is rising as a way of positioning. WLAN is capable to be used within an indoor positioning system soon in real environments. It is a good alternative in terms of accuracy, precision and cost, compared to similar systems. Especially with the common usage of smartphones and tablet PCs, it became the most easy- to-use method, too. In this paper, we present a brief survey on such systems, methodologies, techniques and discuss advantages and disadvantages of each of these.

Multi-objective genetic algorithm based clustering approach and its application to gene expression data

Gene clustering is a common methodology for analyzing similar data based on expression trajectories. Clustering algorithms in general need the number of clusters as a priori, and this is mostly hard to estimate, even by domain experts. In this paper, we use Niched Pareto k-means Genetic Algorithm (GA) for clustering m-RNA data. After running the multi-objective GA, we get the pareto-optimal front that gives alternatives for the optimal number of clusters as a solution set. We analyze the clustering results under two cluster validity techniques commonly cited in the literature, namely DB index and SD index. This gives an idea about ranking the optimal numbers of clusters for each validity index. We tested the proposed clustering approach by conducting experiments using three data sets, namely figure2data, cancer (NCI60) and Leukaemia data. The obtained results are promising; they demonstrate the applicability and effectiveness of the proposed approach.

Recent Trends in Information Reuse and Integration

The present text aims at helping the reader to maximize the reuse of information. Topics covered include tools and services for creating simple, rich, and reusable knowledge representations to explore strategies for integrating this knowledge into legacy systems. The reuse and integration are essential concepts that must be enforced to avoid duplicating the effort and reinventing the wheel each time in the same field. This problem is investigated from different perspectives. in organizations, high volumes of data from different sources form a big threat for filtering out the information for effective decision making. the reader will be informed of the most recent advances in information reuse and integration.

Clustering by integrating multi-objective optimization with weighted k-means and validity analysis

This paper presents a clustering approach that integrates multi-objective optimization, weighted k-means and validity analysis in an iterative process to automatically estimate the number of clusters, and then partition the whole given data to produce the most natural clustering. The proposed approach has been tested on real-life dataset; results of both weighted and unweighed k-means are reported to demonstrate applicability and effectiveness of the proposed approach.

Complex networks driven salient region detection based on superpixel segmentation

In this paper, we propose an efficient method for salient region detection. First, the image is decomposed by using superpixel segmentation which groups similar pixels and generates compact regions. Based upon the generated superpixels, similarity between the regions is calculated by benefiting from color, location, histogram, intensity, and area information of each region as well as community identification via complex networks theory in the over-segmented image. Then, contrast, distribution and complex networks based saliency maps are generated by using the mentioned features. These saliency maps are used to create a final saliency map. The applicability, effectiveness and consistency of the proposed approach are illustrated by conducting some experiments using publicly available datasets. The tests have been used to compare the proposed method with some state-of-the-art methods. The reported results cover qualitative and quantitative assessments which demonstrate that our approach outputs high quality saliency maps and mostly achieves the highest precision rate compared to the other methods. We propose an efficient method for salient region detection.An image is decomposed using superpixel segmentation.Similarity between the regions is calculated based upon the generated superpixels.Contrast, distribution and complex networks based saliency maps are generated.The proposed method has been compared with some state-of-the-art methods.