Regional analysis of cortical thickness has been studied extensively in building imaging biomarkers for early detection of Alzheimer’s disease (AD), but not its inter-regional covariation. We present novel features based on the inter-regional co-variation of cortical thickness. Initially the cortical labels of each patient is partitioned into small patches (graph nodes) by spatial k-means clust...

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...

This chapter describes the basic concepts and application of a family of methods for class discovery, generically known as clustering, applied to microarray data. Although many clustering methods exist, only a few have been extensively used for microarray data analysis (among them I will revise hierarchical clustering, k-means, SOM, SOTA and model-based clustering). Key aspects in clustering su...

clustering is one of the known techniques in the field of data mining where data with similar properties is within the set of categories. k-means algorithm is one the simplest clustering algorithms which have disadvantages sensitive to initial values of the clusters and converging to the local optimum. in recent years, several algorithms are provided based on evolutionary algorithms for cluster...

Crime detection is one of the major issues in the field of criminology. In fact, criminology includes knowing the details of a crime and its intangible relations with the offender. In spite of the enormous amount of data on offenses and offenders, and the complex and intangible semantic relationships between this information, criminology has become one of the most important areas in the field o...

We address the problem of the combination of multiple data partitions, that we call a clustering ensemble. We use a recent clustering approach, known as Spectral Clustering, and the classical K-Means algorithm to produce the partitions that constitute the clustering ensembles. A comparative evaluation of several combination methods is performed by measuring the consistency between the combined ...

Clustering has the following challenges: 1) clusters with arbitrary shapes; 2) minimal domain knowledge to determine the input parameters; 3) scalability for large data sets. Density-based clustering has been recognized as a powerful approach for discovering clusters with arbitrary shapes. However, the other two challenges still remain in most existing clustering algorithms. In this paper, we e...

In student modeling, the concept of “mastery learning” i.e. that a student continues to learn a skill till mastery is attained is important. Usually, mastery is defined in terms of most recent student performance. This is also the case with models such as Knowledge Tracing which estimate knowledge solely based on patterns of questions a student gets correct and the task usually is to predict im...

In this paper, we present the Modified K-Means Clustering algorithm Analysis and performance, the clustering analysis can be used to partition the cluster data with number of choice clusters and perform each cluster if it can form properly or not and it can pertain by using the silhouette coefficient method. In this one the silhouette coefficient can apply on the group of author’s Hand G-indice...

Data mining and machine leaning communities were surprised when Keogh et al. (2003) pointed out that the k-means cluster centers in subsequence time-series clustering become sinusoidal pseudopatterns for almost all kinds of input time-series data. Understanding this mechanism is an important open problem in data mining. Our new theoretical approach (based on spectral clustering and translationa...