Customer Segmentation Menggunakan Fuzzy C-means Clustering Pada E-commerce Henz Collection

Online shop "Henz Collection" is an onlineshop that sells products such as clothes, bags, and shoes. The main problem faced by the online shop are not yet have a website that specialized in selling the products that it sells itself and difficult to know which customers have loyalty to this shop online. Ecommerce application is built using Fuzzy C-Means. Fuzzy C-Means clustering is a technique in which the existence of each data point is determined by the degree of membership (partition). Value centroid k-means clustering taken from random numbers, so that when the centroid determination will be modified so that the clustering results have not changed, so that the customer data can be grouped consistently and data on the cluster have been no changes during the period that would diklaster not changed. The purpose of the application design e-commerce based website is to look at the criteria for customers who are entitled to a rebate / discount. Based on test results and data modules can be concluded that the application is running as expected. Discounting seen from the experimental formation of clusters that provide the best global value silhouette. Cluster in the period 2014 to have the degree of similarity of 60% of customer data and in the period 2015 to have the degree of similarity of 80% of customer data contained in these clusters with the data of the owner onlineshop discount

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure