Mall-Customer-Segregation

MALL CUSTOMER SEGREGATION

TREE USED: Red-black Tree

REFERENCE USED: This whole package is based on a research paper

RESEARCH ARTICLE USED: Enhanced k-means clustering algorithm using red black tree and min-heap

• Article in International Journal of Innovation and Technology Management · January 2011
• BY THE AUTHORS: Rajeev Kumar, Rajeshwar Puran and Joydip Dhar

ALGORITHM USED: Enhanced K-means Clustering Algorithm INTRODUCTION TO THE ALGORITHM:

Fast and high quality clustering is one of the most important tasks in the modern era of information processing. With the huge amount of available data and with an aim to create better quality clusters, scores of algorithms having quality-complexity trade-offs have been proposed. However, the k-means algorithm proposed during the late 1970’s still enjoys a respectable position in the list of clustering algorithms. It is considered to be one of the most fundamental algorithms of data mining. It is basically an iterative algorithm. In each iteration, it requires finding the distance between each data object and the centroid of each cluster. Considering the hugeness of modern databases, this task in itself snowballs into a tedious task. This algorithm employs two data structures viz. red-black tree and min-heap. Extensive experiments have been carried out. The results so obtained establish the superiority of our version of k-means algorithm over the traditional one. In our project, we are using this algorithm to segregate the mall customers. The shopping complexes make use of their customers’ basic data and develop ML models to target the right ones. This not only increases sales but also makes the complexes efficient. Hence, segmentation among them is important.

FEATURES OF THE CUSTOMERS:

1. CustomerID: It is the unique ID given to a customer
2. Gender: Gender of the customer
3. Age: The age of the customer
4. Annual Income(k₹): It is the annual income of the customer
5. Spending Score: It is the score(out of 100) given to a customer by the mall authorities, based on the money spent and the behavior of the customer.