We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering techniqu...

Landslide databases and input parameters used for modeling landslide hazard often contain imprecisions and uncertainties inherent in the decision-making process. Dealing with imprecision and uncertainty requires techniques that go beyond classical logic. In this paper, methods of fuzzy k -means classification were used to assign digital terrain attributes to continuous landform classes whereas ...

This paper presents the fourth participation of the SINAI group, University of Jaén, in the Photo Retrieval task at Image CLEF 2009. Our system uses only the text of the queries, and a clustering system (based on kmeans) that combines different approaches based on a different use of the cluster data of the queries. The official results shown that the combination between the title of each query ...

This paper presents work on developing a software system for predicting crop yield from climate and plantation data. At the core of this system is a method for unsupervised partitioning of data for finding spatio-temporal patterns in climate data using kernel methods which offer strength to deal with complex data. For this purpose, a robust weighted kernel k-means algorithm incorporating spatia...

We explore the use of extended pixel representation for color based image segmentation using the K-means clustering algorithm. Various extended pixel representations have been implemented in this paper and their results have been compared. By extending the representation of pixels an image is mapped to a higher dimensional space. Unlike other approaches, where data is mapped into an implicit fe...

Purpose – The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers. Design/methodology/approach – In total, 98 automobile and auto-parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two-...

We present the first linear time (1+ε)-approximation algorithm for the k-means problem for fixed k and ε. Our algorithm runs in O(nd) time, which is linear in the size of the input. Another feature of our algorithm is its simplicity – the only technique involved is random sampling.

Normalized Cuts is a state-of-the-art spectral method for clustering. By applying spectral techniques, the data becomes easier to cluster and then k-means is classically used. Unfortunately the number of clusters must be manually set and it is very sensitive to initialization. Moreover, k-means tends to split large clusters, to merge small clusters, and to favor convex-shaped clusters. In this ...

One of the main approaches for modeling fracture and crack propagation in solid materials is adaptive insertion of cohesive elements, in which line-like (2D) or surface-like (3D) elements are inserted into the finite element mesh to model the nucleation and propagation of failure surfaces. In this approach, however, cracks are forced to propagate along element boundaries, following paths that i...