Tree-based Clustering Method Research Articles

Heterogeneity of Treatment Effects Among Patients With Type 2 Diabetes and Elevated Body Mass Index in a Study Comparing Group Medical Visits Focused on Weight Management and Medication Intensification.

Illuminating heterogeneity of treatment effect (HTE) within trials is important for identifying target populations for implementation. The aim of this study was to examine HTE in a trial of group medical visits (GMVs) for patients with type 2 diabetes and elevated body mass index. Participants (n=263) were randomized to GMV-based medication management plus low carbohydrate diet-focused weight management (WM/GMV; n=127) or GMV-based medication management alone (GMV; n=136) for diabetes control. We used QUalitative INteraction Trees, a tree-based clustering method, to identify subgroups with greater improvement in hemoglobin A1c (HbA1c) and weight from either WM/GMV or GMV. Subgroup predictors included 32 baseline demographic, clinical, and psychosocial factors. Internal validation was conducted to estimate bias in the range of mean outcome differences between arms. QUalitative INteraction Trees analyses indicated that for patients who had not previously attempted weight loss, WM/GMV resulted in better glycemic control than GMV (mean difference in HbA1c improvement=1.48%). For patients who had previously attempted weight loss and had lower cholesterol and blood urea nitrogen, GMV was better than WM/GMV (mean difference in HbA1c improvement=1.51%). No treatment-subgroup effects were identified for weight. Internal validation resulted in moderate corrections in mean HbA1c differences between arms; however, differences remained in the clinically significant range. This work represents a novel step toward targeting care approaches for patients to maximize benefit based on individual patient characteristics.

44499 Heterogeneity of treatment effect among patients with type 2 diabetes and body mass index >=27kg/m^2 in the Jump Start Study

ABSTRACT IMPACT: This is the first study to use QUINT analyses to examine heterogeneity of treatment effect for group medical visits among individuals with type 2 diabetes. QUINT is a data driven method that assumes no a priori assumptions regarding effect moderators - an important step in the path towards personalized medicine. OBJECTIVES/GOALS: To examine heterogeneity of treatment effect (HTE) in Jump Start, a trial that compared the effectiveness of group medical visits (GMVs) focused on medication management only versus the addition of intensive weight management (WM) on glycemic control for patients with type 2 diabetes and body mass index >=27kg/m^2. METHODS/STUDY POPULATION: Jump Start patients (n=263) were randomized to a GMV-based medication management plus low carbohydrate diet-focused WM program (WM/GMV; n = 127) or GMV-based medication management only (GMV; n = 136) for diabetes control. We used QUalitative INteraction Trees (QUINT), a tree-based clustering method, to determine if there were subgroups of patients who derived greater benefit from either WM/GMV or GMV. Subgroup predictors included 32 baseline demographic, clinical, and psychosocial factors. Outcome was hemoglobin A1c (HbA1c). We conducted internal validation via bootstrap resampling to estimate bias in the range of mean outcome differences among arms. RESULTS/ANTICIPATED RESULTS: QUINT analyses indicated that for patients who had not previously attempted weight loss, WM/GMV resulted in better glycemic control than GMV alone (mean difference in HbA1c improvement = 1.48%). For patients who had previously attempted weight loss and had lower cholesterol and blood urea nitrogen levels, GMV alone was better than WM/GMV (mean difference in HbA1c improvement = 1.51%). Internal validation resulted in moderate corrections in the mean HbA1c differences between arms; however, differences remained in the clinically significant range. DISCUSSION/SIGNIFICANCE OF FINDINGS: Among patients with diabetes and BMI>=27kg/m^2, a low-carbohydrate, weight loss focus may better improve HbA1c in those who have never attempted weight loss. A medication management focus may be better in those who have attempted weight loss and have lower cholesterol and blood urea nitrogen.

MCF Tree-Based Clustering Method for Very Large Mixed-Type Data Set

Several clustering methods have been proposed for analyzing numerous mixed-type data sets composed of numeric and categorical attributes. However, existing clustering methods are not suitable for clustering very large mixed-type data sets because they require a high computational cost or a large memory size. We propose a novel clustering method for very large data sets using a mixed-type clustering feature (MCF) vector with summary information about a cluster. The MCF vector consists of the CF vector and a histogram to summarize the mixed-type values. Based on the MCF vector, we propose an MCF tree, along with a distance measure between the MCF vectors representing two clusters. Unlike previous studies that summarize a data set based on a fixed memory size, we estimate a small initial memory size of the data set for building the tree. Then, the memory size is adaptively increased to estimate a more accurate threshold by reflecting the size reduction in the re-built tree. Our theoretical analysis demonstrates the efficiency of the proposed approach. Experimental results on very large synthetic and real data sets demonstrate that the proposed approach clusters the data sets significantly faster than existing clustering methods while maintaining similar or better clustering accuracy.

Bayesian mixture models and their Big Data implementations with application to invasive species presence-only data

Due to their conceptual simplicity and flexibility, non-parametric mixture models are widely used to identify latent clusters in data. However, when it comes to Big Data, such as Landsat imagery, such model fitting is computationally prohibitive. To overcome this issue, we fit Bayesian non-parametric models to pre-smoothed data, thereby reducing the computational time from days to minutes, while disregarding little of the useful information. Tree based clustering is used to partition the clusters into smaller and smaller clusters in order to identify clusters of high, medium and low interest. The tree-based clustering method is applied to Landsat images from the Brisbane region, which were the actual sources of motivation for development of the method. The images are taken as a part of the red imported fire-ant eradication program that was launched in September 2001 and which is funded by all Australian states and territories, along with the federal government. To satisfy budgetary constraints, modelling is performed to estimate the risk of fire-ant incursion in each cluster so that the eradication program focuses on high risk clusters. The likelihood of containment is successfully derived by combining the fieldwork survey data with the results obtained from the proposed method.

CRAFTER: A Tree-Ensemble Clustering Algorithm for Static Datasets with Mixed Attributes and High Dimensionality

Clustering is an important aspect of data mining, while clustering high-dimensional mixed-attribute data in a scalable fashion still remains a challenging problem. In this paper, we propose a tree-ensemble clustering algorithm for static datasets, CRAFTER, to tackle this problem. CRAFTER is able to handle categorical and numeric attributes simultaneously, and scales well with the dimensionality and the size of datasets. CRAFTER leverages the advantages of a tree-ensemble to handle mixed attributes and high dimensionality. The concept of the class probability estimates is utilized to identify the representative data points for clustering. Through a series of experiments on both synthetic and real datasets, we have demonstrated that CRAFTER is superior than Random Forest Clustering (RFC), an existing tree-based clustering method, in terms of both the clustering quality and the computational cost.

A novel clustering method to identify cell types from single cell transcriptional profiles

With the advancement of the high throughput single cell techniques, transcriptomics data generation at the single cell level becomes very easy. Analysis of this single cell expression values can reveal lots of unprecedented information about complex cellular heterogeneity and tissue composition. To date, different statistical methods are applied to analyze expression data at the cellular level, but there is still pretty much scope for the development of new bioinformatics tools. In this article, a graph theoretic clustering algorithm is proposed to identify cellular states from single cell gene expression data. The proposed algorithm first generates a shared nearest neighbor graph from the single cell RNA-seq dataset and then applies minimum spanning tree based clustering method to cluster the graph nodes. To compare our proposed algorithm’s performance with other unsupervised clustering methods, we used three real scRNA-seq datasets (human cancer cells, human embryonic cells and mouse embryonic cells). From the comparison result, it is evident that the proposed algorithm outperforms other standard single cell analysis methods.

A novel dynamic minimum spanning tree based clustering method for image mining

Image mining is more than just an extension of data mining to image domain. Image mining is a technique commonly used to extract knowledge directly from image. Image segmentation is the first step in image mining. Image segmentation is very difficult but important problem in computer vision. We present a novel Minimum Spanning Tree based approach to image segmentation which can be applied to either greyscale or color image. The assumption is the nearby pixels with similar color or greyscale intensities may belong to the same region or segment of the image. A graph representation for an image is derived from the similarity between the pixels, and then partitioned by computationally efficient graph clustering method. We treat image segmentation as graph partitioning problem. The minimum spanning tree based clustering algorithm is capable of detecting clusters with irregular boundaries. In this paper we propose minimum spanning tree based clustering algorithm using weighted Euclidean distance for edges, which is key element in building the graph from image. The algorithm produces best number of clusters (regions) with segments. An important characteristic of the method is its ability to preserve detail in low-variability image regions while preserving detail in high-variability regions.

PEACE: Parallel Environment for Assembly and Clustering of Gene Expression

We present PEACE, a stand-alone tool for high-throughput ab initio clustering of transcript fragment sequences produced by Next Generation or Sanger Sequencing technologies. It is freely available from www.peace-tools.org. Installed and managed through a downloadable user-friendly graphical user interface (GUI), PEACE can process large data sets of transcript fragments of length 50 bases or greater, grouping the fragments by gene associations with a sensitivity comparable to leading clustering tools. Once clustered, the user can employ the GUI's analysis functions, facilitating the easy collection of statistics and allowing them to single out specific clusters for more comprehensive study or assembly. Using a novel minimum spanning tree-based clustering method, PEACE is the equal of leading tools in the literature, with an interface making it accessible to any user. It produces results of quality virtually identical to those of the WCD tool when applied to Sanger sequences, significantly improved results over WCD and TGICL when applied to the products of Next Generation Sequencing Technology and significantly improved results over Cap3 in both cases. In short, PEACE provides an intuitive GUI and a feature-rich, parallel clustering engine that proves to be a valuable addition to the leading cDNA clustering tools.