iosr-JCE   Volume-6 ~ Issue-2

Abstract:Images are analyzed with edge and color values. Pixel information is used in the color property extraction. Texture and contrast are pixel based features. Shape or edge features are used to represent images. The images are assigned with their category values. The image features are used in the classification process. Classification techniques are used to assign labels to the images. Color constancy methods are largely dependent on the distribution of colors and color edges in an image. Natural image statistics and scene semantics are used in the color consistency methods. Color contrast and texture values are used in natural image statistics model..
Keywords: Classification technique, Color property extraction, Mixture of Gaussian, Natural image statistic, Scene semantics.

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Abstract:Clustering is one of the data mining techniques used to group similar objects into different meaningful classes known as clusters. Objects in each cluster have maximum similarity while the objects across the clusters have minimum or no similarity. This kind of partitioning of objects into various groups has many real time applications such as pattern recognition, machine learning and so on. In this paper we review a clustering algorithm based on genetic K-means [1] and compare it with GKMODE and IGKA. The algorithm works well for both numeric and discrete values. The existing genetic K-means algorithms have limitation as they can cluster only numeric data. The algorithm [1] overcomes this problem and provides a better way of characterization of clusters. The empirical results revealed that the performance of the proposed algorithm has been improved. We also make observations and recommendations on the proposed algorithm, GKMODE, and IGKA that help in future enhancements.
Keywords - Generic algorithm, data mining, clustering, mixed data

[1] Dharmendra K Roy & Lokesh K Sharma. Genetic K-Means Clustering Algorithm for Mixed Numeric and Categorical Data Sets.
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Abstract: Some of this traffic accident crisis in Nigeria is caused by the increase in number of vehicles and inefficient drivers on the road, bad condition and poor maintenance of the roads. The significance of the study lies on the profiling of clusters of traffic roads in terms of accident related data and the degree in which these accident characteristics are perceptive between the different created clusters. Applying data mining process to model traffic accident data records helped in obtaining the characteristics of drivers' behaviour, road condition and weather condition that are connected with different injury severities and death. The traffic roads are divided into a low accident risk and high accident risk traffic roads, determining accidents in different age categories and period of accidents. A design of a data mining model for analysis and prediction of accidents rate in Nigeria was presented. In this study, we profiled traffic roads, differentiated the data set into preprocessing and transforming data set; created the association rules; and post-processed the frequent accident item sets. The data mining function was used and data cleaned using feature selection.
Keywords:Data mining, road accident, profiling, vehicles, clusters, traffic road

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