International Journal of Artificial Intelligence and Applications for Smart Devices

10

Authors: Nidal Ismael, Mahmoud Alzaalan and WesamAshour

BIRCH algorithm is a clustering algorithm suitable for very large data sets. In the algorithm, a CF-tree is built whose all entries in each leaf node must satisfy a uniform threshold T, and the CF-tree is rebuilt at each stage by different threshold. But using a single threshold cause many shortcomings in the birch algorithm, in this paper to propose a solution to this shortcoming by using multiple thresholds instead of a single threshold.

16

Authors: Kanaan EL. Bhissy, Fadi EL. Faleet and WesamAshour

Clustering and segmentation algorithms that depend on Gaussian kernel function as a way for constructing affinity matrix, these algorithms like spectral clustering algorithms suffer from the poor estimation of parzen window σ. The final results depend on this parameter and differ on each time we change it.In this paper we present a new algorithm for estimation σ using optimization techniques, we construct a vector σ ̂i , each σ ̂_i corresponding to ith row in a dissimilarity matrix which is used to construct an affinity matrix using Gaussian kernel function. O The affinity matrix which is produced using our algorithm is very informative and contains addition information like the number of clusters.

18

Authors: Dr. Satuluri Naganjaneyulu, Dr. Mrithyumjaya Rao Kuppa and Dr. Ali MIrza Mahmood

In Data mining and Knowledge Discovery hidden and valuable knowledge from the data sources is discovered. The traditional algorithms used for knowledge discovery are bottle necked due to wide range of data sources availability. Class imbalance is a one of the problem arises due to data source which provide unequal class i.e. examples of one class in a training data set vastly outnumber examples of the other class(es). Researchers have rigorously studied several techniques to alleviate the problem of class imbalance, including resampling algorithms, and feature selection approaches to this problem. In this paper, we present a new hybrid frame work dubbed as Wrapper based Intelligent Under Sampling (WIUS) for learning from skewed training data. These algorithms provide a simpler and faster alternative by using C4.5 and wrapper as base algorithm. We conduct experiments using ten UCI data sets from various application domains using five algorithms for comparison on five evaluation metrics. Experimental results show that our method has higher Area under the ROC Curve, F-measure, precision, TP rate and TN rate values than many existing class imbalance learning methods.