Clustering Techniques in Bioinformatics

Abstract

Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with data in such a manner that it can help researchers identify the required information. As a step forward to address this challenge, experts have utilized clustering techniques as a mean of exploring hidden structure and patterns in underlying data. Improved stability, robustness and accuracy of unsupervised data classification in many fields including pattern recognition, machine learning, information retrieval, image analysis and bioinformatics, clustering has proven itself as a reliable tool. To identify the clusters in datasets algorithm are utilized to partition data set into several groups based on the similarity within a group. There is no specific clustering algorithm, but various algorithms are utilized based on domain of data that constitutes a cluster and the level of efficiency required. Clustering techniques are categorized based upon different approaches. This paper is a survey of few clustering techniques out of many in data mining. For the purpose five of the most common clustering techniques out of many have been discussed. The clustering techniques which have been surveyed are: K-medoids, Kmeans, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Self-Organizing Map (SOM) clustering.

Cell Typing and Sub-typing Based on Detecting Characteristic Subspaces of Morphological Features Derived from Neuron Images

“…Typical way is to rst extract morphological or topological features, like L-measure features that describe global attributes of neuronal structures (Scorcioni et al, 2008), and then choose a clustering algorithm to classify neurons (Peng et ). Considering the di culty in validating results from unsupervised learning and the applicability of clustering algorithms, nding an optimal algorithm has always been focus of the study (Masood & Khan, 2015). In contrast, representation and feature learning for clustering have not been explored extensively (Karim et al, 2021 Wan et al, 2015) to obtain better clustering results.…”

Research on optimization of two-way clustering algorithm for gene expression data analysis

“…Nowadays, as a high-throughput experimental method, gene chips can obtain tens of thousands of pieces of genetic data in one experiment. Because of its high efficiency and massive size, it is widely used in many frontier fields of biology, such as analyzing the regulation of gene expression profiles, disease diagnosis and treatment, and drug development [4,5]. While gene chip technology provides strong support for gene function research, it also produces a large amount of complex data.…”