Data Reduction Method for Categorical Data Clustering

Categorical data clustering constitutes an important part of data mining; its relevance has recently drawn attention from several researchers. As a step in data mining, however, clustering encounters the problem of large amount of data to be processed. This article offers a solution for categorical clustering algorithms when working with high volumes of data by means of a method that summarizes the database. This is done using a structure called CM-tree. In order to test our method, the KModes and Click clustering algorithms were used with several databases. Experiments demonstrate that the proposed summarization method improves execution time, without losing clustering quality

Dynamic Visualization of Changes in Association Patterns

The present proposal deals with high-dimensional binary data collected in different occasions in time or space. Studying the associations of data collected at different occasions, a primary aim is to detect changes in the association structure from one occasion to another. A suitable exploratory technique for the analysis of multiple associations in high-dimensional data is the multiple correspondence analysis (MCA; Greenacre, 2007). However, the comparison of MCA factorial displays referring to different occasions is meaningless. A possible solution to link the association structures of different data batches is to start from an MCA display of a reference and incrementally update the solution with further batches (Iodice D'Enza and Greenacre, 2010). This approach, does not take into account the presence of a cluster structure in the set of statistical units. This contribution intend to present an approach that, through the combination of clustering and factorial techniques, aims to visualize the evolution of the association structure of binary attributes over different data batches. The proposal is to introduce a latent categorical variable which is determined and updated at each incoming batch; in other words this variable is determined according to the association structure and represents the 'link' among the solutions. The latent categorical variable is endogenously determined by the procedure; in particular, it refers to the cluster structure characterizing the data set in question. A starting solution is updated incrementally as new data sets are analysed. The factorial display will describe the patterns of change in the multiple associations when shifting the analysis from one occasion to the other. Procedures suitably combining clustering with factorial analysis techniques have been proposed. Vichi and Kiers (2001) propose a combination of principal component analysis (PCA) with k-means clustering method. In the framework of categorical data, another interesting approach combining clustering and multiple correspondence analysis (MCA) is proposed by Hwang et al. (2006). Similarly, yet dealing with binary data, Palumbo and Iodice D'Enza (2010) propose a suitable dimension reduction and clustering. The present proposal is an enhancement of the latter approach to the comparative analysis of multiple batches

Machine Learning Models for High-dimensional Biomedical Data

abstract: The recent technological advances enable the collection of various complex, heterogeneous and high-dimensional data in biomedical domains. The increasing availability of the high-dimensional biomedical data creates the needs of new machine learning models for effective data analysis and knowledge discovery. This dissertation introduces several unsupervised and supervised methods to help understand the data, discover the patterns and improve the decision making. All the proposed methods can generalize to other industrial fields. The first topic of this dissertation focuses on the data clustering. Data clustering is often the first step for analyzing a dataset without the label information. Clustering high-dimensional data with mixed categorical and numeric attributes remains a challenging, yet important task. A clustering algorithm based on tree ensembles, CRAFTER, is proposed to tackle this task in a scalable manner. The second part of this dissertation aims to develop data representation methods for genome sequencing data, a special type of high-dimensional data in the biomedical domain. The proposed data representation method, Bag-of-Segments, can summarize the key characteristics of the genome sequence into a small number of features with good interpretability. The third part of this dissertation introduces an end-to-end deep neural network model, GCRNN, for time series classification with emphasis on both the accuracy and the interpretation. GCRNN contains a convolutional network component to extract high-level features, and a recurrent network component to enhance the modeling of the temporal characteristics. A feed-forward fully connected network with the sparse group lasso regularization is used to generate the final classification and provide good interpretability. The last topic centers around the dimensionality reduction methods for time series data. A good dimensionality reduction method is important for the storage, decision making and pattern visualization for time series data. The CRNN autoencoder is proposed to not only achieve low reconstruction error, but also generate discriminative features. A variational version of this autoencoder has great potential for applications such as anomaly detection and process control.Dissertation/ThesisDoctoral Dissertation Industrial Engineering 201

Generalized Forward Sufficient Dimension Reduction for Categorical and Ordinal Responses

We present a forward sufficient dimension reduction method for categorical or ordinal responses by extending the outer product of gradients and minimum average variance estimator to multinomial generalized linear model. Previous work in this direction extend forward regression to binary responses, and are applied in a pairwise manner to multinomial data, which is less efficient than our approach. Like other forward regression-based sufficient dimension reduction methods, our approach avoids the relatively stringent distributional requirements necessary for inverse regression alternatives. We show consistency of our proposed estimator and derive its convergence rate. We develop an algorithm for our methods based on repeated applications of available algorithms for forward regression. We also propose a clustering-based tuning procedure to estimate the tuning parameters. The effectiveness of our estimator and related algorithms is demonstrated via simulations and applications