Linear Principal Components Analysis (LPCA) is known for its simplicity to reduce the features dimensionality. An extension of LPCA, Kernel Principal Components Analysis (KPCA), outperforms LPCA when applied on non-linear data in high dimensional feature space. However, on large datasets with high input space, KPCA deals with a memory issue and imbalance classification problems with difficulty....

Assigning a set of objects to groups such that objects in one group or cluster are more similar to each other than the other clusters’ objects is the main task of clustering analysis. SSPCO optimization algorithm is anew optimization algorithm that is inspired by the behavior of a type of bird called see-see partridge. One of the things that smart algorithms are applied to solve is the problem ...

The key points in customer segmentation are determining target groups and satisfying their needs. Recency-Frequency-Monetary (RFM) analysis K-Means clustering algorithm the popular methods for when analyzing behavior. In our study, we adapt K-means to RFM model by extracting features that represent aspects of home appliances. Customers with similar RFM-oriented assigned same clusters, while cus...

Clustering is a widely studied data mining problem in the text documents. The problem finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In this paper, we will provide a detailed survey of the problem of text clustering. We will study the key challenges of the clustering problem, as it applies to the...

Clustering is an extensively studied data mining problem in the text domains. The difficulty finds numerous applications in customer segmentation, classification, collaborative filtering, visualization, document organization, and indexing. In text mining, clustering the sentence is one of the processes and used within general text mining tasks. Several clustering methods and algorithms are used...

Retaining users and customers is one of the most important challenges for the service industry from mobile communications to online gaming. As the users of these services form dynamic networks that grow in size, predicting churners becomes harder and harder. The changing behavior of users and type of services changing day by day make it di cult to monitor the mobility of customers. However, fro...

The retail industry collects huge amounts of data on sales, customer buying history, goods transportation, consumption, and service. With increased availability and ease of use of modern computing technology and e-commerce, the availability and popularity of such businesses has grown rapidly. Many retail stores have websites where customers can make online purchases. These factors have resulted...