In order to master the development effect of polymer flooding well group, it is necessary accurately analyze influence different factors in whole process on index. Combined with principle big data analysis, based neighborhood rough set theory and Kmeans clustering algorithm, an intelligent analysis algorithm proposed determine achievement indicators group. Firstly, was used reduce attributes in...

How to consider both the influence of weather and wind power in modeling process probability distribution forecast error (WPFE), emphasize application value conditional modeling, is rarely studied at present. This paper proposes a novel method for WPFE. uses proposed MNSGA-II-Kmeans algorithm perform multi-objective clustering multi-dimensional influencing factors (MDIF), including power. It ca...

In this work, we explore the benefits of combining clustering and social trust information for Recommender Systems. We demonstrate the performance advantages of traditional clustering algorithms like kMeans and we explore the use of new ones like Affinity Propagation (AP). Contrary to what has been used before, we investigate possible ways that social-oriented information like explicit trust co...

This paper introduces modified versions of the K-Means (KM) and Moving K-Means (MKM) clustering algorithms, called the Two-Dimensional K-Means (2D-KM) and Two-Dimensional Moving KMeans (2D-MKM) algorithms respectively. The performances of these two proposed algorithms are compared with three of the commonly used conventional clustering algorithms, namely K-Means (KM), Fuzzy C-Means (FCM), and M...

Much research has been undertaken in the field of blind source separation (BSS) and a large number of algorithms have been developed. However, most of them assume that the number of sources is known. In this paper we present an algorithm to estimate the number of sources in the (over-)determined and underdetermined case. We call this algorithm NOSET (Number of Sources Estimation Technique). We ...

Clustering is a division of data into groups of similar objects. Kmeans has been used in many clustering work because of the ease of the algorithm. Our main effort is to parallelize the k-means clustering algorithm. The parallel version is implemented based on the inherent parallelism during the Distance Calculation and Centroid Update phases. The parallel K-means algorithm is designed in such ...

The partition-based clustering algorithms, like KMeans and fuzzy K-Means, are most widely and successfully used in data mining in the past decades. In this paper, we present a robust and sparse fuzzy K-Means clustering algorithm, an extension to the standard fuzzy K-Means algorithm by incorporating a robust function, rather than the square data fitting term, to handle outliers. More importantly...

Clustering big data using data mining algorithms is a modern approach, used in various science and medical fields. k-means clustering algorithm is a good approach for clustering, but choosing initial centers and provides less accuracy guarantees. The enhanced k-means approach called k-means++ chooses one center uniformly at random provides better functionality, but fails to handle data of large...

In this paper, a novel feature selection approach for supervised interval valued features is proposed. The proposed approach takes care of selecting the class specific features through interval K-Means clustering. The kernel of K-Means clustering algorithm is modified to adapt interval valued data. During training, a set of samples corresponding to a class is fed into the interval KMeans cluste...