Thousands of malicious applications (apps) are daily created, modified with the aid automation tools, and released on World Wide Web. Several techniques have been applied over years to identify whether an APK is or not. The use these intends unknown malware mainly by calculating similarity a sample previously grouped, already known families apps. Thus, high rates accuracy would enable several c...

BACKGROUND Clustering is a key step in the processing of Expressed Sequence Tags (ESTs). The primary goal of clustering is to put ESTs from the same transcript of a single gene into a unique cluster. Recent EST clustering algorithms mostly adopt the alignment-free distance measures, where they tend to yield acceptable clustering accuracies with reasonable computational time. Despite the fact th...

Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In ...

A new simple test to detect within-family clustering of infected individuals is proposed. The test is derived as the score test for several different parametric models designed to allow an increased within-family infectivity. The new test is compared with other tests proposed to detect the same type of clustering caused by increased within-family infectivity. Applications of household disease d...

Traditional clustering ensembles methods combine all obtained clustering results at hand. However, we observe that it can often achieve a better clustering solution if only part of all available clustering results are combined. This paper proposes a novel clustering ensembles method, termed as resampling-based selective clustering ensembles method. The proposed selective clustering ensembles me...