2 research outputs found
CSHURI - Modified HURI algorithm for Customer Segmentation and Transaction Profitability
Association rule mining (ARM) is the process of generating rules based on the
correlation between the set of items that the customers purchase.Of late, data
mining researchers have improved upon the quality of association rule mining
for business development by incorporating factors like value (utility),
quantity of items sold (weight) and profit. The rules mined without considering
utility values (profit margin) will lead to a probable loss of profitable
rules. The advantage of wealth of the customers' needs information and rules
aids the retailer in designing his store layout[9]. An algorithm CSHURI,
Customer Segmentation using HURI, is proposed, a modified version of HURI [6],
finds customers who purchase high profitable rare items and accordingly
classify the customers based on some criteria; for example, a retail business
may need to identify valuable customers who are major contributors to a
company's overall profit. For a potential customer arriving in the store, which
customer group one should belong to according to customer needs, what are the
preferred functional features or products that the customer focuses on and what
kind of offers will satisfy the customer, etc., finds the key in targeting
customers to improve sales [9], which forms the base for customer utility
mining.Comment: 11 pages, 5 tables, 1 figure, IJCSEIT-201
Data Masking with Privacy Guarantees
We study the problem of data release with privacy, where data is made
available with privacy guarantees while keeping the usability of the data as
high as possible --- this is important in health-care and other domains with
sensitive data. In particular, we propose a method of masking the private data
with privacy guarantee while ensuring that a classifier trained on the masked
data is similar to the classifier trained on the original data, to maintain
usability. We analyze the theoretical risks of the proposed method and the
traditional input perturbation method. Results show that the proposed method
achieves lower risk compared to the input perturbation, especially when the
number of training samples gets large. We illustrate the effectiveness of the
proposed method of data masking for privacy-sensitive learning on
benchmark datasets