A Survey on Clustering Techniques for Big Data Mining

Sajana, T.; Rani, C. M. Sheela; Narayana, Komanapalli Venkata Lakshmi

doi:10.17485/ijst/2016/v9i3/75971

Cited by 100 publications

(47 citation statements)

References 24 publications

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“…There are various clustering algorithms which differ from each other in terms of the approach they follow in order to do the grouping of the objects according to their characteristics. These are stated as follows [23] -In Partitioned Based Clustering all the data points are taken as a single cluster in the beginning. These data points are then separated into clusters by iteratively positioning these objects between the clusters.…”

Section: Using Clustering Techniques For Customer Segmentationmentioning

confidence: 99%

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Approaches to Clustering in Customer Segmentation

Tripathi¹,

Bhardwaj²,

Poovammal³

2018

IJET

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Customer Relationship Management(CRM) has always played a crucial role as a market strategy for providing organizations with the quintessential business intelligence for building, managing and developing valuable long-term customer relationships. A number of business enterprises have come to realize the significance of CRM and the application of technical expertise to achieve competitive advantage. This study explores the importance of Customer Segmentation as a core function of CRM as well as the various models for segmenting customers using clustering techniques. The available clustering models for customer segmentation, in general, and the major models of K-Means and Hierarchical Clustering, in particular, are studied and the virtues and vices of the techniques are pointed out. Finally, the possibility of developing a hybrid solution by the combination of the above two techniques, having the ability to outperform the individual models, is discussed.

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Section: Using Clustering Techniques For Customer Segmentationmentioning

confidence: 99%

“…Hence it is easy to choose and decide on the number of clusters that we wish to take by looking at the dendrogram. However, for a large number of observations its computational speed is very low as compared to the nonhierarchical methods of clustering [23]. Hence, the size and order of the data have an impact on the final results obtained.…”

mentioning

confidence: 99%

Approaches to Clustering in Customer Segmentation

Tripathi¹,

Bhardwaj²,

Poovammal³

2018

IJET

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“…Partition based algorithms can found clusters of Non convex shapes. [8] Some algorithm which can use this concept are planned as follows :…”

Section: Methodsmentioning

confidence: 99%

“…By comparing most similarity other object is assigning to the cluster. For each data vector the algorithm calculates the distance between data vector and each cluster centroid.This algorithm works as follows [8] : Algorithm: Generate K-Means Input: Training Data Output: Decision Value • Load dataset, Take the experiment of gathering a sample of observed values • select number of k clusters • Randomly generates k-cluster and evaluate cluster center and generate k random points as cluster centres using k-means function.…”

Section: K-meansmentioning

confidence: 99%

Quality Healthcare Prediction using K Means And Clara Partition Based Clustering Algorithm For Big Data Analytics

Chinchmalatpure¹,

Dhore²

2020

IJEAT

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 Abstract: Big Data is a collection of large or vast amount of information that grows at ever increasing rates. Big data is ordered, unstructured, semi structured or mixed data in natural world. Researchers are designing, implementing, analyzing different application. In medicinal industry large or vast amount of data is available but people are not able to extract the significant information. Healthcare big data analytics (HBDA) becomes "Healthier analytics" by fusion of techniques. In this paper, we discuss and implement algorithms of clustering using R-Studio tool. Clustering is defined as the method of partitioning set of patterns into similar groups called as clusters. We can extract the data from vast datasets in the form of clustering rules. These clustering techniques are scalable. Also, we compare the accuracies of two partition based clustering techniques k-means and Clara on healthcare datasets for giving good quality of healthcare services. Implemented results demonstrate the k-means method gives better accuracy values than Clara algorithm.

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“…DBSCAN is one of effective density-based cluster algorithm, its simple and create cluster in arbitrary shape also cluster information as noise and outliers, DBSCAN is fast and its efficient with large Data Base, it has two inputs first radius second the minimum points found in radius, DBSCAN general idea based on finding minimum number of point in radius space to form clusters else if the number of minimum points not found in radius it will mark as noise [15] [3], DBSCAN algorithm is stated below [16] . Algorithm 2: DBSCAN outlier [16]  Set of points to be considered to form a graph.…”

Section: Dbscan Algorithmmentioning

confidence: 99%

Proposed KDBSCAN Algorithm for Clustering

Hussein

Jalil

2018

ijs

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Science, technology and many other fields are use clustering algorithm widely for many applications, this paper presents a new hybrid algorithm called KDBSCAN that work on improving k-mean algorithm and solve two of its problems, the first problem is number of cluster, when it`s must be entered by user, this problem solved by using DBSCAN algorithm for estimating number of cluster, and the second problem is randomly initial centroid problem that has been dealt with by choosing the centroid in steady method and removing randomly choosing for a better results, this work used DUC 2002 dataset to obtain the results of KDBSCAN algorithm, it`s work in many application fields such as electronics libraries, biology and marketing, the KDBSCAN algorithm that described in this paper has better results than traditional K-mean and DBSCAN algorithms in many aspects, its preform stable result with lower entropy.

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A Survey on Clustering Techniques for Big Data Mining

Cited by 100 publications

References 24 publications

Approaches to Clustering in Customer Segmentation

Approaches to Clustering in Customer Segmentation

Quality Healthcare Prediction using K Means And Clara Partition Based Clustering Algorithm For Big Data Analytics

Proposed KDBSCAN Algorithm for Clustering

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