Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

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Survey of clustering algorithms

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review.

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A Survey of Outlier Detection Methodologies

An overview of the self-organizing map algorithm, on which the papers in this issue are based, is presented in this article.

The Self-Organizing Map

BET Bromodomain Inhibition as a Therapeutic Strategy to Target c-Myc

Color image segmentation: advances and prospects

The self-organizing map (SOM) is an excellent tool in exploratory phase of data mining. It projects input space on prototypes of a low-dimensional regular grid that can be effectively utilized to visualize and explore properties of the data. When the number of SOM units is large, to facilitate quantitative analysis of the map and the data, similar units need to be grouped, i.e., clustered. In this paper, different approaches to clustering of the SOM are considered. In particular, the use of hierarchical agglomerative clustering and partitive clustering using K-means are investigated. The two-stage procedure-first using SOM to produce the prototypes that are then clustered in the second stage-is found to perform well when compared with direct clustering of the data and to reduce the computation time.

/pdf/clustering-of-the-self-organizing-map-1na7a1h6p3.pdf

Clustering of the self-organizing map

SOM Toolbox for Matlab 5

The Self-Organizing Map (SOM) is a vector quantization method which places the prototype vectors on a regular low-dimensional grid in an ordered fashion. This makes the SOM a powerful visualization tool. The SOM Toolbox is an implementation of the SOM and its visualization in the Matlab 5 computing environment. In this article, the SOM Toolbox and its usage are shortly presented. Also its performance in terms of computational load is evaluated and compared to a corresponding Cprogram.

/pdf/self-organizing-map-in-matlab-the-som-toolbox-4930ubdkkn.pdf

Self-organizing map in Matlab: the SOM Toolbox

The Self-Organizing Map (SOM) is a powerful neural network method for analysis and visualization of high-dimensional data. It maps nonlinear statistical dependencies between high-dimensional measurement data into simple geometric rela- tionships on a usually two-dimensional grid. The mapping roughly preserves the most important topological and metric relationships of the original data elements and, thus, inherently clusters the data. The need for visualization and clustering occurs, for instance, in the analysis of various engineering problems. In this paper, the SOM has been applied in monitoring and modeling of complex industrial processes. Case studies, including pulp process, steel production, and paper industry are described.

Process Monitoring and Modeling Using the Self-Organizing Map

Purpose: Radium-223 dichloride (radium-223, Xofigo), a targeted alpha therapy, is currently used for the treatment of patients with castration-resistant prostate cancer (CRPC) with bone metastases. This study examines the mode-of-action and antitumor efficacy of radium-223 in two prostate cancer xenograft models.Experimental Design: Mice bearing intratibial LNCaP or LuCaP 58 tumors were randomized into groups (n = 12-17) based on lesion grade and/or serum PSA level and administered radium-223 (300 kBq/kg) or vehicle, twice at 4-week intervals. X-rays and serum samples were obtained biweekly. Soft tissue tumors were observed macroscopically at sacrifice. Tibiae were analyzed by gamma counter, micro-CT, autoradiography and histology.Results: Radium-223 inhibited tumor-induced osteoblastic bone growth and protected normal bone architecture, leading to reduced bone volume in LNCaP and abiraterone-resistant LuCaP 58 models. Furthermore, radium-223 resulted in lower PSA values and reduced total tissue and tumor areas, indicating that treatment constrains prostate cancer growth in bone. In addition, radium-223 suppressed abnormal bone metabolic activity as evidenced by decreased number of osteoblasts and osteoclasts and reduced level of the bone formation marker PINP. Mode-of-action studies revealed that radium-223 was deposited in the intratumoral bone matrix. DNA double-strand breaks were induced in cancer cells within 24 hours after radium-223 treatment, and PSA levels were significantly lower 72 hours after treatment, providing further evidence of the antitumor effects.Conclusions: Taken together, radium-223 therapy exhibits a dual targeting mode-of-action that induces tumor cell death and suppresses tumor-induced pathologic bone formation in tumor microenvironment of osseous CRPC growth in mice. Clin Cancer Res; 23(15); 4335-46. ©2017 AACR.

https://clincancerres.aacrjournals.org/content/clincanres/23/15/4335.full.pdf

Esa Alhoniemi

Papers

Clustering of the self-organizing map

SOM Toolbox for Matlab 5

Self-organizing map in Matlab: the SOM Toolbox

Process Monitoring and Modeling Using the Self-Organizing Map

Radium-223 Inhibits Osseous Prostate Cancer Growth by Dual Targeting of Cancer Cells and Bone Microenvironment in Mouse Models