Alp Kut

ST-DBSCAN: An algorithm for clustering spatial-temporal data

This paper presents a new density-based clustering algorithm, ST-DBSCAN, which is based on DBSCAN. We propose three marginal extensions to DBSCAN related with the identification of (i) core objects, (ii) noise objects, and (iii) adjacent clusters. In contrast to the existing density-based clustering algorithms, our algorithm has the ability of discovering clusters according to non-spatial, spatial and temporal values of the objects. In this paper, we also present a spatial-temporal data warehouse system designed for storing and clustering a wide range of spatial-temporal data. We show an implementation of our algorithm by using this data warehouse and present the data mining results.

Spatio-temporal outlier detection in large databases

Outlier detection is one of the major data mining methods. This paper proposes a three-step approach to detect spatio-temporal outliers in large databases. These steps are clustering, checking spatial neighbors, and checking temporal neighbors. In this paper, we introduce a new outlier detection algorithm to find small groups of data objects that are exceptional when compared with rest large amount of data. In contrast to the existing outlier detection algorithms, new algorithm has the ability of discovering outliers according to the non-spatial, spatial and temporal values of the objects. In order to demonstrate the new algorithm, this paper also presents an example application using a data warehouse

An incremental genetic algorithm for classification and sensitivity analysis of its parameters

Traditionally, data mining tasks such as classification and clustering are performed on data warehouses. Usually, updates are collected and applied to the data warehouse frequent time periods. For this reason, all patterns derived from the data warehouse have to be updated frequently as well. Due to the very large volumes of data, it is highly desirable to perform these updates incrementally. This study proposes a new incremental genetic algorithm for classification for efficiently handling new transactions. It presents the comparison results of traditional genetic algorithm and incremental genetic algorithm for classification. Experimental results show that our incremental genetic algorithm considerably decreases the time needed for training to construct a new classifier with the new dataset. This study also includes the sensitivity analysis of the incremental genetic algorithm parameters such as crossover probability, mutation probability, elitism and population size. In this analysis, many specific models were created using the same training dataset but with different parameter values, and then the performances of the models were compared.

An approach for parallel execution of Web services

This paper presents a model which combines the processing power of parallel computation with the ease of Web service usage. In this model, parallel programming environment can be embedded in a visual environment. Parallelization of Web services is provided by using multithreading technology with dataset parameters. This work also provides parallel usage of computers located in different places via a wide area network such as Internet.

SOM++: integration of self-organizing map and k-means++ algorithms

Data clustering is an important and widely used task of data mining that groups similar items together into subsets. This paper introduces a new clustering algorithm SOM++, which first uses K-Means++ method to determine the initial weight values and the starting points, and then uses Self-Organizing Map (SOM) to find the final clustering solution. The purpose of this algorithm is to provide a useful technique to improve the solution of the data clustering and data mining in terms of runtime, the rate of unstable data points and internal error. This paper also presents the comparison of our algorithm with simple SOM and K-Means + SOM by using a real world data. The results show that SOM++ has a good performance in stability and significantly outperforms three other methods training time.

Improving quality assurance in education with web-based services by data mining and mobile technologies

The main focus of this paper is to use web-based services, data mining techniques and mobile technologies to improve Quality Assurance (QA) in education. This paper presents rather sophisticated web-based tools and services dedicated to the QA in education. It proposes a model for efficient building of the key elements of the QA follow-up: surveys, questionnaires, the visualization of the obtained results, reporting and further usage of the obtained data. It also presents some practical applications to demonstrate the models capabilities.

Development of a Component Oriented Garment Design Library

In todays garment industry manufacturers are not able to withhold the designs of the models that are created consequently, manufacturers have to spend considerable time presenting the model designs to new customers. For this reason, a software package has been developed to facilitate from customers with the help of this program, garment companies will be able to establish archives of their various models on the computer. Also, it will be possible to design new models more efficiently by using the computer rather than doing technical drawings by hand.

A new approach for surface construction in medical applications: Fair tiling

Several tools and applications help doctors in medical area. 3D models generated by various visualization methods are currently used in disease diagnosis and treatment. During 3D model generation, several problems arise. One of the major problems of generating 3D models from consecutive 2D slices is surface construction. This study presents a surface construction method, fair tiling, which is used to generate surfaces of from arbitrary points. These points are gathered from CT images to construct vessel-like structures. Fair tiling presents an easier structure compared to other methods in the area.

Decision Support System For A Customer Relationship Management Case Study

Data mining is a computational approach aiming to discover hidden and valuable information in large datasets. It has gained importance recently in the wide area of computational among which many in the domain of Business Informatics. This paper focuses on applications of data mining in Customer Relationship Management (CRM). The core of our application is a classifier based on the naive Bayesian classification. The accuracy rate of the model is determined by doing cross validation. The results demonstrated the applicability and effectiveness of the proposed model. Naive Bayesian classifier reported high accuracy. So the classification rules can be used to support decision making in CRM field. The aim of this study is to apply the data mining model to the banking sector as example case study. This work also contains an example data set related with customers to predict if the client will subscribe a term deposit. The results of the implementation are available on a mobile platform.

A new approach for weighted clustering using decision tree

In the field of cluster analysis, most clustering algorithms consider the contribution of each attribute for classification uniformly. In fact, different attributes (or different features) should be of different contribution for clustering result. In order to consider the different roles of each attribute, this paper proposes a new approach for clustering algorithms based on weights, in which decision tree technique is used to assign the weights to each attribute. The comparison results show that novel approach improves the robustness of the traditional clustering algorithms. The experimental results with various test data sets illustrate the effectiveness of the proposed approach.