Cyclic Association Rules Research Articles

From concrete to inferred knowledge: Enhanced mining constraint-based cyclic association rules from medical social network

In recent years, social network has been given much interest. The explosion of social network activity has lead to generation of massive volumes of user-related data and has given birth to a new area of data analysis. In parallel, the last decade witnessed a fastidious interest in the data mining which efficiently find hidden knowledge that can be extracted from applied information, namely, social data. Among the most used data mining techniques, we particularly focus on cyclic constraint-based association rules. In this paper, we aim to derive significant cyclic constraint-based association rules from social data. Thus, we introduce a new approach EMC 2 for social mining through constraint-based cyclic association rules extraction. The encouraging experimental results carried out prove the usefulness of our approach.

Analysis and Evaluation of Novel Privacy Preserving Techniques for Collaborative Temporal Association Rule Mining Using Secret Sharing

With digital data being abundant in today's world, competing organizations desire to gain insights about the market, without putting the privacy of their confidential data at risk. This paper provides a new dimension to the problem of Privacy Preserving Distributed Association Rule Mining (PPDARM) by extending it to a distributed temporal setup. It proposes extensions of public key based and non-public key based additively homomorphic techniques, based on efficient private matching and Shamir's secret sharing, to privately decipher these global cycles in cyclic association rules. Along with the theoretical analysis, it presents experimental results to substantiate it. This paper observes that the Secret Sharing scheme is more efficient than the one based on Paillier homomorphic encryption. However, it observes a considerable increase in the overhead associated with the Shamir's secret sharing scheme, as a result of the increase in the number of parties. To reduce this overhead, it extends the secret sharing scheme without mediators to a novel model with a Fully Trusted and a Semi Trusted Third Party. The experimental results establish this functioning for global cycle detections in a temporal setup as a case study. The novel constructions proposed can also be applied to other scenarios that want to undertake Secure Multiparty Computation (SMC) for PPDARM.

Fuzzy temporal association rules: combining temporal and quantitative data to increase rule expressiveness

Data mining for association rules aims to discover interesting relationships among sets of items in a database. Very often these databases include some kind of temporal information, the most common being a temporal label indicating transaction date. Within the field of association rule mining temporal information has been used to obtain sequential association rules, periodic or cyclic association rules, calendric association rules, or event‐driven association rules. The temporal component is also relevant when analyzing how association rules evolve if datasets are evaluated on different time‐slices. On the other hand, in traditional association rules item attributes were usually Boolean, but many attributes in current databases are quantitative in nature. Fuzzy temporal association rules arise from the use of fuzzy sets to describe quantitative temporal and/or not temporal attributes of items in a database, and/or to introduce fuzzy temporal specifications for the rules a user is interested in; the use of fuzzy sets allows a linguistic interpretation of the rules and also provides means to handle the uncertainty present in attribute measurements. Depending on the rule pattern the final user is interested in, different methods for fuzzy temporal association rule mining can be found in the literature, with mining algorithms adapted to the rule model being used. WIREs Data Mining Knowl Discov 2014, 4:64–70. doi: 10.1002/widm.1116This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Association Rules Technologies > Computational Intelligence

EVALUATION ON DIFFERENT APPROACHES OF CYCLIC ASSOCIATION RULES

Now a day’s companies have large amount of data its exploration becomes complicated, especially if we emphasize the temporal aspect while considering association rules. Therefore, we are introducing the temporal association rules mining. In this paper, we focus on cyclic association rules, classified as a category of the temporal association rules. An association rule is cyclic if the rule has the minimum confidence and support at regular time intervals. Such a rule need not hold for the entire transactional Database, but only for a particular Periodic time interval. The cyclic association rules used to discover relations among items characterized by regular cyclic variation overt time. For finding cyclic association rules we are going through various different approaches and their comparative analysis. This Techniques will enable marketers to better Identify trends in sales and allow for better forecasting of Future demand. comparative study of different approaches are also provided.

Cyclic Association Rules Mining under Constraints

Several researchers have explored the temporal aspect of association rules mining. In this paper, we focus on the cyclic association rules, in order to discover correlations among items characterized by regular cyclic variation overtime.The overview of the state of the art has revealed the dra wbacks of proposed algorithm literatures, namely the excessive number of generated rules which are not meeting the expert’s expectations. To overcome these restrictions, we have introduced our approach dedicated to generate the cyclic association rules under constraints through a new method called Constraint-Based Cyclic Association Rules CBCAR. The carried out experiments underline the usefulness and the performance of our new approach. General Terms Data Mining, Association Rules, Algorithms.

Privacy Preservation for Global Cyclic Associations in Distributed Databases

There have been immense advancements in collaborative data analysis. Hence the desire to keep one's own sensitive data secret is an important challenge of preserving privacy while mining in a distributed setup. There have been indeed numerous attempts to propose privacy preserving data mining algorithms. However, in order to extract the associations with respect to the time segment, the cyclic nature of the association rules must be examined. To the best of our knowledge and explorations, the current privacy preserving algorithms, do not consider the cyclic nature of the association rules. Hence, we here propose techniques with illustrative examples that help to privately decipher cyclic association rules that are common to all the participating parties. The techniques proposed by us for a multi-party scenario are based on homomorphism and Shamir's secret sharing.

Towards An Incremental Maintenance of Cyclic Association Rules

Recently, the cyclic association rules have been introduced in order to discover rules from items characterized by their regular variation over time. In real life situations, temporal databases are often appended or updated. Rescanning the whole database every time is highly expensive while existing incremental mining techniques can efficiently solve such a problem. In this paper, we propose an incremental algorithm for cyclic association rules maintenance. The carried out experiments of our proposal stress on its efficiency and performance.