The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

In this document we present an overview of Transactional Memory models, as well as a comparison to traditional parallel frameworks. In this context, we follow with an analysis on the problem of self-tuning concurrency control in transactional memory, where different mechanisms are used to predict and adjust an application’s concurrency level with respect to available parallelism. We are particu...

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

The problem of determining the optimal number of clusters is important but mysterious in cluster analysis. In this paper, we propose a novel method to find a set of candidate optimal number Ks of clusters in transactional datasets. Concretely, we propose Transactional-cluster-modes Dissimilarity based on the concept of coverage density as an intuitive transactional inter-cluster dissimilarity m...

Traditional association rules are mainly concerned about intra-transactional rules. In time series analysis, intra-transactional association rules can only reveal the correlations of multiple time series at same time. It is difficult to forecast the trend of time series. In this paper, it is studied the mining problem of inter-transactional association rules in time series with which the trend ...