Spatial data mining seeks to discover meaningful patterns from data where a key dimension of the data is geographical location. This spatial dimension becomes important when data either refer to specific locations andJor have significant spatial dependence and which needs to be taken into consideration if meaningfid patterns are to emerge. For point data there are two main groups of approaches....

brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

In Medical diagnosis, through Magnetic Resonance Images Robustness and accuracy of the Prediction algorithms are very important, because the result is crucial for treatment of Patients. There are many popular classification and clustering algorithms used for predicting the diseases from Images. The goal of clustering a medical image is to simplify the representation of an image into a meaningfu...

Data mining has gained popularity in the database field recently, the gained knowledge is static because of the static nature of the database, and does not reflect the dynamic nature of knowledge. Extension data mining is a product combining Extenics with data mining, By using the theory and method of Extenics, it can mine the knowledge from database which is relative to solve contradictory pro...

The paradigm shift from ̳data-centered pattern mining‘ to ̳domain driven actionable knowledge discovery‘ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand....

is one of the fundamental components in time series data mining. One of the uses of the time series segmentation is trend analysis-to segment the time series into primitive trends like uptrend and downtrend. In this paper, a time series segmentation method based on a specialized binary tree representation scheme is proposed; this representation scheme is customized for financial time series to ...

In the airline industry, data analysis and data mining are a prerequisite to push customer relationship management (CRM) ahead. Knowledge about data mining methods, marketing strategies and airline business processes has to be combined to successfully implement CRM. This paper is a case study and gives an overview about distinct issues, which have to be taken into account in order to provide a ...

Advances in wireless transmission and increasing quantity of GPS in vehicles flood us with massive amount of trajectory data. The large amounts of trajectories imply considerable quantity of interesting road condition that current traffic database lacks. Mining live traffic condition from trajectories is a challenge due to complexity of road network model, uncertainty of driving behavior as wel...

The paradigm shift from ‘data-centered pattern mining’ to ‘domain driven actionable knowledge discovery’ has increased the need for considering the business yield (utility) and demand or rate of recurrence of the items (frequency) while mining a retail business transaction database. Such a data mining process will help in mining different types of itemsets of varying business utility and demand...

Analysing customers in groups is one of the most fundamental issues in Marketing. It helps companies sufficiently learn from their customers, and rationally design their marketing strategies. Given a customer database with n records (customers) and m attributes (one’s characteristics) stored, different approaches can be applied to automatically segment (cluster) records in divisions. In this pa...