Social patterns: Community detection using behavior-generated network datasets

Leung, Alice; Dron, William; Hancock, J. P.; Aguirre, Matthew; Purnell, Jon; Han, Jiawei; Wang, Chi; Srivastava, Jaideep; Mahapatra, Amogh; Roy, Atanu Singha; Scott, Lisa

doi:10.1109/nsw.2013.6609198

Cited by 4 publications

(1 citation statement)

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“…Data mining is the process of finding hidden patterns and trends in databases and using that information to do a variety of tasks such as finding association rules, clustering heterogeneous groups of information and build predictive models [9]. It can also be considered as an important tool for data segmentation, selection, exploration and building models using the vast data stores to discover previously unknown patterns in various domains such as healthcare [12] [21], [23], [20] [19], social media analysis [18], [24], [2], [16], [11], [15], [17], finances and various other domains [22]. From the past few decades, data mining has been used extensively in various areas of decision making and decision analysis by financial institutions, for credit scoring and fraud detection; marketers, for direct marketing and cross-selling or up-selling; retailers, for market segmentation and store layout; and manufacturers, for quality control and maintenance scheduling.…”

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confidence: 99%

Using Gaussian Measures for Efficient Constraint Based Clustering

Sarkar,

Roy

2014

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In this paper we present a novel iterative multiphase clustering technique for efficiently clustering high dimensional data points. For this purpose we implement clustering feature (CF) tree on a real data set and a Gaussian density distribution constraint on the resultant CF tree. The post processing by the application of Gaussian density distribution function on the micro-clusters leads to refinement of the previously formed clusters thus improving their quality. This algorithm also succeeds in overcoming the inherent drawbacks of conventional hierarchical methods of clustering like inability to undo the change made to the dendogram of the data points. Moreover, the constraint measure applied in the algorithm makes this clustering technique suitable for need driven data analysis. We provide veracity of our claim by evaluating our algorithm with other similar clustering algorithms.

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