Using ENRICH, a computerized marriage assessment tool (Olson, Fournier, & Druckman, 1986), data from 8,383 couples was collected across nine dimensions of their marriage. The data was analyzed in two phases-cluster structure seeking and classification phases-by three different clustering methods (inverse factor analysis, hierarchical agglomerative, and k-means cluster analysis). Seven types of ...

One of the approaches used to improve the accuracy and relevancy in information retrieval is cluster analysis. Clustering methods determine relationships among text documents, and allow the determination of similar groups or clusters of documents. These methods are computationally expensive, thereby limiting their use to a relatively small set of documents. This paper describes a multi-agent sy...

Stunting has been widely known as the highest case of malnutrition suffered by toddlers in world and a bad impact on children's future. In 2018, Indonesia was ranked 31st stunting 4th Southeast Asia. About 30.8% (roughly 3 out 10) children under 5 years suffer from Indonesia. To support government policy making handling stunting, it is undoubtedly necessary to classify levels regions this work,...

Clustering groups records that are similar to each other into the same group, and those that are less similar into different groups. Clustering data of mixed types is difficult due to different data characteristics. Extending Gower’s metric for nominal and ordinal data is incorporated into an agglomerative hierarchical clustering algorithm to cluster mixed type data. This paper describes the ex...

Coding of data, usually upstream of data analysis, has crucial implications for the data analysis results. By modifying the data coding – through use of less than full precision in data values – we can aid appreciably the effectiveness and efficiency of the hierarchical clustering. In our first application, this is used to lessen the quantity of data to be hierarchically clustered. The approach...

− We present the applicability of hierarchical agglomerative cluster algorithms to terahertz (THz) spectroscopic analysis. We show the influence of different windowing and filtering methods in the spectral data preprocessing to enhance the clustering results. Two distance measures are compared. Classical Euclidean distance on the full frequency range and a distance working only on the minima of...

Clustering is one of the most interesting and important tool for research in data mining and other disciplines. The aim of clustering is to find the relationship among the data objects, and classify them into meaningful subgroups. The effectiveness of clustering algorithms depends on the appropriateness of the similarity measure between the data in which the similarity can be computed. This pap...

In this paper, we present a framework for unsupervised domain adaptation of PLDA based i-vector speaker recognition systems. Given an existing out-of-domain PLDA system, we use it to cluster unlabeled in-domain data, and then use this data to adapt the parameters of the PLDA system. We explore two versions of agglomerative hierarchical clustering that use the PLDA system. We also study two auto...

This paper presents the results of an experimental study of some common document clustering techniques. In particular, we compare the two main approaches to document clustering, agglomerative hierarchical clustering and K-means. (For K-means we used a “standard” K-means algorithm and a variant of K-means, “bisecting” K-means.) Hierarchical clustering is often portrayed as the better quality clu...