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Abstract-The emergence of many business competitors has engendered severe rivalries among competing businesses in gaining new customers and retaining old ones. Due to the preceding, the need for exceptional customer services becomes pertinent, notwithstanding the size of the business. Furthermore, the ability of any business to understand each of its customers' needs will earn it greater leverage in providing targeted customer services and developing customised marketing programs for the customers. This understanding can be possible through systematic customer segmentation. Each segment comprises customers who share similar market characteristics. The ideas of Big data and machine learning have fuelled a terrific adoption of an automated approach to customer segmentation in preference to traditional market analyses that are often inefficient especially when the number of customers is too large. In this paper, the kMeans clustering algorithm is applied for this purpose. A MATLAB program of the k-Means algorithm was developed (available in the appendix) and the program is trained using a zscore normalised two-feature dataset of 100 training patterns acquired from a retail business. The features are the average amount of goods purchased by customer per month and the average number of customer visits per month. From the dataset, four customer clusters or segments were identified with 95% accuracy, and they were labeled: High-Buyers-Regular-Visitors (HBRV), High-Buyers-Irregular-Visitors (HBIV), Low-BuyersRegular-Visitors (LBRV) and Low-Buyers-Irregular-Visitors (LBIV).

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agent's world and equip it with the capability to anticipate or predict the results of its actions. However, to design a robust autonomous learning technique for an unknown, dynamic, partially observable or noisy environment remains a daunting task. This paper proposes a Temporospatial Merge Grow When Required (TMGWR) network for continuous self-organisation of an agent's sensorimotor awareness in noisy environments. TMGWR is an adaptive neural algorithm that learns the sensorimotor map of an agent's world using a time series self-organising strategy and the Grow When Required (GWR) algorithm. The algorithm is compared with GNG, GWR and TGNG in terms of their disambiguation performance, sensorial representation accuracy and sensorimotor-link error, a new metric that is developed in this paper to evaluate how well a sensorimotor map represents causality in the agent's world. The outcomes of the experiments show that TMGWR is more efficient and suitable for sensorimotor map learning in noisy environments than the competing algorithms.