BigFCM: Fast, Precise and Scalable FCM on Hadoop

Clustering plays an important role in mining big data both as a modeling technique and a preprocessing step in many data mining process implementations. Fuzzy clustering provides more flexibility than non-fuzzy methods by allowing each data record to belong to more than one cluster to some degree. However, a serious challenge in fuzzy clustering is the lack of scalability. Massive datasets in emerging fields such as geosciences, biology and networking do require parallel and distributed computations with high performance to solve real-world problems. Although some clustering methods are already improved to execute on big data platforms, but their execution time is highly increased for large datasets. In this paper, a scalable Fuzzy C-Means (FCM) clustering named BigFCM is proposed and designed for the Hadoop distributed data platform. Based on the map-reduce programming model, it exploits several mechanisms including an efficient caching design to achieve several orders of magnitude reduction in execution time. Extensive evaluation over multi-gigabyte datasets shows that BigFCM is scalable while it preserves the quality of clustering

Applying subclustering and Lp distance in Weighted K-Means with distributed centroids

We consider the Weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric in a way that feature weights become feature re-scaling factors for any considered exponent. Moreover, the traditional Silhouette clustering validity index was adapted to deal with both numerical and categorical types of features. Finally, we show that our new method usually outperforms traditional K-Means as well as the recently proposed WK-DC clustering algorithm.Peer reviewe

An inter-domain supervision framework for collaborative clustering of data with mixed types.

We propose an Inter-Domain Supervision (IDS) clustering framework to discover clusters within diverse data formats, mixed-type attributes and different sources of data. This approach can be used for combined clustering of diverse representations of the data, in particular where data comes from different sources, some of which may be unreliable or uncertain, or for exploiting optional external concept set labels to guide the clustering of the main data set in its original domain. We additionally take into account possible incompatibilities in the data via an automated inter-domain compatibility analysis. Our results in clustering real data sets with mixed numerical, categorical, visual and text attributes show that the proposed IDS clustering framework gives improved clustering results compared to conventional methods, over a wide range of parameters. Thus the automatically extracted knowledge, in the form of seeds or constraints, obtained from clustering one domain, can provide additional knowledge to guide the clustering in another domain. Additional empirical evaluations further show that our approach, especially when using selective mutual guidance between domains, outperforms common baselines such as clustering either domain on its own or clustering all domains converted to a single target domain. Our approach also outperforms other specialized multiple clustering methods, such as the fully independent ensemble clustering and the tightly coupled multiview clustering, after they were adapted to the task of clustering mixed data. Finally, we present a real life application of our IDS approach to the cluster-based automated image annotation problem and present evaluation results on a benchmark data set, consisting of images described with their visual content along with noisy text descriptions, generated by users on the social media sharing website, Flickr