Patterns in data obtained from wine chemical and sensory evaluations are difficult to decipher using classical statistics. Coupling fusion with machine learning techniques could assist solving these issues lead new hypotheses. The current study investigated the applicability of pattern recognition approaches for oenological applications. A sample set 23 Chenin blanc wines made young (< 35 ye...

It is a common occurrence in plant breeding programs to observe missing values in three-way three-mode multi-environment trial (MET) data. We proposed modifications of models for estimating missing observations for these data arrays, and developed a novel approach in terms of hierarchical clustering. Multiple imputation (MI) was used in four ways, multiple agglomerative hierarchical clustering,...

Conceptual clustering is a discovery process that groups a set of data in the way that the intra-cluster similarity is maximized and the inter-cluster similarity is minimized. Traditional clustering algorithms employ some measure of distance between data points in n-dimensional space. However, not all data types can be represented in a metric space, therefore no natural distance function is ava...

The present study utilized hierarchical agglomerative cluster (HAC) analysis to categorize users of a popular, web-based computer-assisted pronunciation training (CAPT) program into user types using activity log data. Results indicate an optimal grouping of four types: Reluctant, Point-focused, Optimal, and Engaged. Clustering was determined by aggregate data on seven indicator variables of mix...

The rapid development of microarray technologies enabled the monitoring of expression levels of thousands of genes simultaneously. Microarray technology has great potential for creating an enormous amount of data in a short time, and now becomes a new tool for studying such broad problems as classification of tumors in biology and medical science. Many statistical methods are available for anal...

High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new informationtheoretic divisive algorithm for feature/word clustering and apply it to text classification. Exist...

BACKGROUND Vulvodynia classification is based on the sensory dimensions of pain and does not include psychological factors associated with the pain experience and treatment outcomes. Previous work has shown that individuals with chronic pain can be classified into subgroups based on pain sensitivity, psychological distress, mood, and symptom severity. OBJECTIVE The aim of this study was to id...

Disambiguating person names in a set of documents (such as a set of web pages returned in response to a person name) is a key task for the presentation of results and the automatic profiling of experts. With largely unstructured documents and an unknown number of people with the same name the problem presents many difficulties and challenges. This chapter treats the task of person name disambig...

Machine learning techniques are ever prevalent as datasets continue to grow daily. Associative classification (AC), which combines and association rule mining algorithms, plays an important role in understanding big that generate a large number of rules. Clustering, on the other hand, can contribute by reducing space produce compact models. The above-mentioned facts were main motivation for thi...

Information about the state and planning of the speaker is obscured in traditional classifications of disfluencies which are generally at the word level. This study delves into the acoustic and prosodic information of repetitions, one of the most common disfluencies. A hierarchical clustering of prosodic features reveals three subsets of repetitions, each reflecting different problems in planning.