A novel harmony search-K means hybrid algorithm for clustering gene expression data

Abstract

Recent progress in bioinformatics research has led to the accumulation of huge quantities of biological data at various data sources. The DNA microarray technology makes it possible to simultaneously analyze large number of genes across different samples. Clustering of microarray data can reveal the hidden gene expression patterns from large quantities of expression data that in turn offers tremendous possibilities in functional genomics, comparative genomics, disease diagnosis and drug development. The k- ¬means clustering algorithm is widely used for many practical applications. But the original k-¬means algorithm has several drawbacks. It is computationally expensive and generates locally optimal solutions based on the random choice of the initial centroids. Several methods have been proposed in the literature for improving the performance of the k-¬means algorithm. A meta-heuristic optimization algorithm named harmony search helps find out near-global optimal solutions by searching the entire solution space. Low clustering accuracy of the existing algorithms limits their use in many crucial applications of life sciences. In this paper we propose a novel Harmony Search-K means Hybrid (HSKH) algorithm for clustering the gene expression data. Experimental results show that the proposed algorithm produces clusters with better accuracy in comparison with the existing algorithms.

Categorical Data Clustering Using Harmony Search Algorithm for Healthcare Datasets

“…Harmony memory for categorical data clustering is created using randomly generated solutions. The size of the HM is the size of the HM (HMS) × number of data objects (m) in the dataset (Nazeer et al, 2013). It implies all row of harmony memory are a complete clustering solution of the dataset as shown in Eqn (3):…”

K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

“…Chandran and Nazeer [144] proposed an enhanced Kmeans clustering algorithm based on hybridization of the K-means with improved HS optimization technique for finding global optimum solutions. Nazeer, Sebastian, and Kumar [145] presented HSKH-harmony search K-means hybrid for gene expression clustering, which produced a more accurate gene clustering solution. Raval, Raval, and Valiveti [146] proposed a combination of HS and K-means for optimizing wireless sensor network clustering.…”

Threshold Computation to Discover Cluster Structure: A New Approach

“…These methods are useful in varied applications [1] which require handling influx of new data consistently and to perform forecasting, decision making and predictions. These application domains include finance [2], biology [3], feedback analysis [4], sensitivity analysis [5], electricity power consumption etc.…”