Cluster Based Segmentation using K-Means Algorithm (original) (raw)
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Customer Segmentation using K-Means Algorithm
2020
We live in a world where large and vast amount of data is collected daily. Analysing such data is an important need. In the modern era of innovation, where there is a large competition to be better then everyone, the business strategy needs to be according to the modern conditions. The business done today runs on the basis of innovative ideas as there are large number of potential customers who are confounded to what to buy and what not to buy. The companies doing the business are also not able to diagnose the target potential customers. This is where the machine learning comes into picture, the various algorithms are applied to identify the hidden patterns in the data for better decision making. The concept of which customer segment to target is done using the customer segmentation process using the clustering technique. In this paper, the clustering algorithm used is K-means algorithm which is the partitioning algorithm, to segment the customers according to the similar characteri...
APPLICATION OF K-MEANS ALGORITHM IN DATA MINING
, http://www.euroasiapub.org (An open access scholarly, peer-reviewed, interdisciplinary, monthly, and fully refereed journal.) ABSTRACT It is an algorithm to classify or to group your objects based on attributes/features into K number of classified clusters based on the similarity of some attributes. Here K is positive integer number. The types of diabetes disorder symptoms are grouped based on the category of diabetes. The grouping is done by minimizing the sum of squares of distance between data and the corresponding cluster centroid. Thus, the purpose of K-mean clustering is to classify the data. Here the attributes are significant factors causing diabetes such as body mass index, diabetes pedigree function, Plasma glucose concentration in saliva and age .These factors must be grouped based on acquiring a type of diabetes or not .The acquired factor k. partitions the data into classes with high intra-class similarity or low inter-class similarity. An algorithm starts with a random solution, and iteratively makes small changes to the solution, each time improving it a little. When the algorithm cannot see any improvement anymore, it terminates. Ideally, at that point the current solution is close to optimal solution. The k‐means algorithm is a simple iterative method to partition a given dataset into a user specified number of clusters, k. • Here it is tested with a small cluster of symptoms and types of diabetes disorder. One needs to find a suitable stopping criterion for large dataset in medical diagnosis. Here it groups the type-i and type-ii diabetes in one group which is commonly present in most population also mody and gestational diabetes which occur in selective group in another cluster. K-means remains the most widely used partition clustering algorithm in practice. The algorithm is simple, easily understandable and reasonably scalable., http://www.euroasiapub.org (An open access scholarly, peer-reviewed, interdisciplinary, monthly, and fully refereed journal.)
Cluster Analysis in Data Mining using K-Means Method
International Journal of Computer Applications, 2013
To find the unknown and hidden pattern from large amount of data of insurance organizations. There are strong customer base required with the help of large database. Cluster Analysis is an excellent statistical tool for a large and multivariate database. The clusters analysis with K-Means method may be used to develop the model which is useful to find the relationship in a database. In this paper, consider the data of LIC customer, the seeds are the first three customers then compute the distance from cluster using the attributes of customers with the help of Clustering with K-Means method. Comparing the mean distance of cluster with the seeds. Finally, we find the nigh distances from the cluster as the cluster (C1) have three customers named S1, S2, S10 which are satisfy with all the benefits, terms and conditions of cluster (C1). If requirements of any customer same as the S1, S2, S10 then we allocated the cluster (C1). It will increase the revenue as well as profit of the organization with customer satisfaction.
Sustainability
E-commerce system has become more popular and implemented in almost all business areas. E-commerce system is a platform for marketing and promoting the products to customer through online. Customer segmentation is known as a process of dividing the customers into groups which shares similar characteristics. The purpose of customer segmentation is to determine how to deal with customers in each category in order to increase the profit of each customer to the business. Segmenting the customers assist business to identify their profitable customer to satisfy their needs by optimizing the services and products. Therefore, customer segmentation helps E-commerce system to promote the right product to the right customer with the intention to increase profits. There are few types of customer segmentation factors which are demographic psychographic, behavioral, and geographic. In this study, customer behavioral factor has been focused. Therefore users will be analyzed using clustering algori...
Clustering with K-Means Algorithm
There are many situations where we need to separate data into clusters without any labels being provided. This is an example of Unsupervised learning. In this assignment we apply K-Means algorithm for unsupervised learning on the given dataset and analyse the effect of various parameters including number of clusters and initialization method on the accuracy of clustering.
International Journal of Advanced Research in Artificial Intelligence, 2015
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 k-Means 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-Buyers-Regular-Visitors (LBRV) and Low-Buyers-Irregular-Visitors (LBIV).
Data Mining Process Using Clustering: A Survey
irpds.com
Clustering is a basic and useful method in understanding and exploring a data set. Clustering is division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. Interest in clustering has increased recently in new areas of applications including data mining, bioinformatics, web mining, text mining, image analysis and so on. This survey focuses on clustering in data mining. The goal of this survey is to provide a review of different clustering algorithms in data mining. A Categorization of clustering algorithms has been provided closely followed by this survey. The basics of Hierarchical Clustering include Linkage Metrics, Hierarchical Clusters of Arbitrary and Binary Divisive Partitioning is discussed at first. Next discussion is Algorithms of the Partitioning Relocation Clustering include Probabilistic Clustering, K-Medoids Methods, K-Means Methods. Density-Based-Partitioning, Grid-Based Methods and Co-Occurrence of Categorical Data are other sections. Their comparisons are mostly based on some specific applications and under certain conditions. So the results may become quite different if the conditions change.
Segment k-means Clustering Algorithm
2010
Recently a large amount of research has been devoted to moving objects clustering analysis. Typically, clusters have been defined by their motion characteristics and represented by trajectories. These trajectories are collected and clustered to determine typical behaviors. This paper shows how to cluster moving objects trajectories' segments using k-means clustering whereas we define k in advance based on some characteristics of trajectories' segments. Hence, the proposed clustering algorithm is competitive with the traditional k-means clustering as it specifies the value of 'k' in advance. The 'k' value is based on an extracted key feature of moving object trajectories namely, segment's slope.