A hyperclique pattern [H. Xiong et al. (2003)] is a new type of association pattern that contains items which are highly affiliated with each other. More specifically, the presence of an item in one transaction strongly implies the presence of every other item that belongs to the same hyperclique pattern. We present a new algorithm for mining maximal… (More)
Many fundamental questions on aging are still unanswered or are under intense debate. These questions are frequently not addressable by examining a single gene or a single pathway, but can best be addressed at the systems level. Here we examined the modular structure of the protein-protein interaction (PPI) networks during fruitfly and human brain aging. In… (More)
Mining dynamic interdimension association rules for local-scale weather prediction is to discover abnormal weather phenomena changing so that the professional weather forecaster can use these rules to predict some severe weather situations, such as hail storm, thunder storm and so on. A weather analysis is composed of individual analyses of the several… (More)
With the explosive growth of data, the traditional clustering algorithms running on separate servers can not meet the demand. To solve the problem, more and more researchers implement the traditional clustering algorithms on the cloud computing platforms, especially for K-means clustering. But, few researchers pay attention to the K-means clustering… (More)
Cloud computing has been widely used in every social field. The problem of energy consumption in a cloud computing environment has brought cost pressure to service providers and affected the natural environment. However, the reasonable and efficient scheduling of resources could save a lot of energy for cluster. Meanwhile, it's necessary for us to take into… (More)
This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
Partition outlier using neighborhood radius has proven to be an effective distance-based detection algorithm. However, it is not yet clear how to choose the neighborhood radius d<sub>min</sub>, and getting the value by trial and error is still been widely adopted. This paper presents a method to get the neighborhood radius from fractal dimensions which is… (More)
Most real-world datasets have outliers. Outliers can imply abnormal states that often indicate significant performance degradation or danger in certain circumstances. Therefore, the outlier detection plays an important role in the field of data mining. This paper proposes a hybrid distance-based outlier detection approach. It uses the average distance as… (More)