9,160 research outputs found
Privacy Preserving Utility Mining: A Survey
In big data era, the collected data usually contains rich information and
hidden knowledge. Utility-oriented pattern mining and analytics have shown a
powerful ability to explore these ubiquitous data, which may be collected from
various fields and applications, such as market basket analysis, retail,
click-stream analysis, medical analysis, and bioinformatics. However, analysis
of these data with sensitive private information raises privacy concerns. To
achieve better trade-off between utility maximizing and privacy preserving,
Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent
years. In this paper, we provide a comprehensive overview of PPUM. We first
present the background of utility mining, privacy-preserving data mining and
PPUM, then introduce the related preliminaries and problem formulation of PPUM,
as well as some key evaluation criteria for PPUM. In particular, we present and
discuss the current state-of-the-art PPUM algorithms, as well as their
advantages and deficiencies in detail. Finally, we highlight and discuss some
technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page
A Constraint Guided Progressive Sequential Mining Waterfall Model for CRM
CRM has been realized as a core for the growth of any enterprise. This requires both the customer satisfaction and fulfillment of customer requirement, which can only be achieved by analyzing consumer behaviors. The data mining has become an effective tool since often the organizations have large databases of information on customers. However, the traditional data mining techniques have no relevant mechanism to provide guidance for business understanding, model selection and dynamic changes made in the databases. This article helps in understanding and maintaining the requirement of continuous data mining process for CRM in dynamic environment. A novel integrative model, Constraint Guided Progressive SequentialMiningWaterfall (CGPSMW) for knowledge discovery process is proposed. The key performance factors that include management of marketing, sales, knowledge, technology among others those are required for the successful implementation of CRM. We have studied how the sequential pattern mining performed on progressive databases instead of static databases in conjunction with these CRM performance indicators can result in highly efficient and effective useful patterns. This would further help in classification of customers which any enterprise should focus on to achieve its growth and benefit. An organization has limited number of resources that it can only use for valuable customers to reap the fruits of CRM. The different steps of the proposed CGP-SMW model give a detailed elaboration how to keep focus on these customers in dynamic scenarios
Bidirectional Growth based Mining and Cyclic Behaviour Analysis of Web Sequential Patterns
Web sequential patterns are important for analyzing and understanding users
behaviour to improve the quality of service offered by the World Wide Web. Web
Prefetching is one such technique that utilizes prefetching rules derived
through Cyclic Model Analysis of the mined Web sequential patterns. The more
accurate the prediction and more satisfying the results of prefetching if we
use a highly efficient and scalable mining technique such as the Bidirectional
Growth based Directed Acyclic Graph. In this paper, we propose a novel
algorithm called Bidirectional Growth based mining Cyclic behavior Analysis of
web sequential Patterns (BGCAP) that effectively combines these strategies to
generate prefetching rules in the form of 2-sequence patterns with Periodicity
and threshold of Cyclic Behaviour that can be utilized to effectively prefetch
Web pages, thus reducing the users perceived latency. As BGCAP is based on
Bidirectional pattern growth, it performs only (log n+1) levels of recursion
for mining n Web sequential patterns. Our experimental results show that
prefetching rules generated using BGCAP is 5-10 percent faster for different
data sizes and 10-15% faster for a fixed data size than TD-Mine. In addition,
BGCAP generates about 5-15 percent more prefetching rules than TD-Mine.Comment: 19 page
A study on incremental mining of frequent patterns
Data generated from both the offline and online sources are incremental in nature. Changes in the underlying database occur due to the incremental data. Mining frequent patterns are costly in changing databases, since it requires scanning the database from the start. Thus, mining of growing databases has been a great concern. To mine the growing databases, a new Data Mining technique called Incremental Mining has emerged. The Incremental Mining uses previous mining result to get the desired knowledge by reducing mining costs in terms of time and space. This state of the art paper focuses on Incremental Mining approaches and identifies suitable approaches which are the need of real world problem.Keywords: Data Mining, Frequent Pattern, Incremental Mining, Frequent Pattern Minung, High Utility Mining, Constraint Mining
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