P. V. Kumar

A novel fuzzy gaussian-based dissimilarity measure for discovering similarity temporal association patterns

Mining temporal association patterns from time-stamped temporal databases, first introduced in 2009, remain an active area of research. A pattern is temporally similar when it satisfies certain specified subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns in the context of temporal databases. The brute force approach requires performing

A Survey on Temporal Databases and Data mining

2^{n }

Mining Outlier Temporal Association Patterns

2n true support computations for ‘n’ items; hence, an NP-class problem. Also, the apriori or fp-tree-based algorithms designed for static databases are not directly extendable to temporal databases to retrieve temporal patterns similar to a reference prevalence of user interest. This is because the support of patterns violates the monotonicity property in temporal databases. In our case, support is a vector of values and not a single value. In this paper, we present a novel approach to retrieve temporal association patterns whose prevalence values are similar to those of the user specified reference. This allows us to significantly reduce support computations by defining novel expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. We then introduce a novel dissimilarity measure, which is the fuzzy Gaussian-based dissimilarity measure. The measure also holds the monotonicity property. Our evaluations demonstrate that the proposed method outperforms brute force and sequential approaches. We also compare the performance of the proposed approach with the SPAMINE which uses the Euclidean measure. The proposed approach uses monotonicity property to prune temporal patterns without computing unnecessary true supports and distances.

A novel fuzzy similarity measure and prevalence estimation approach for similarity profiled temporal association pattern mining

The problem of mining frequent patterns in a static database is studied extensively in the literature by many researchers. Conventional frequent pattern algorithms are not applicable to find frequent patterns from the temporal database. Temporal database is a database which can store past, present and future information. A temporal relation may be viewed as a database of time invariant and time variant relation instances. The objective of this research is to come up with a novel approach so as to find the temporal association patterns similar to a given reference support sequence and user defined threshold using the concept of Venn diagrams. The proposed approach scans the temporal database only once to find the temporal association patterns and hence reduces the huge overhead incurred when the database is scanned multiple times.

Shifted Adaption Homomorphism Encryption for Mobile and Cloud Learning

Temporal database is a database which captures and maintains past, present and future data. Conventional databases are not suitable for handling such time varying data. In this context temporal database has gained a significant importance in the field of databases and data mining. The major objective of this research is to perform a detailed survey on temporal databases and the various temporal data mining techniques and explore the various research issues in temporal data mining. We also throw light on the temporal association rules and temporal clustering works carried in literature.

A similarity measure for temporal pattern discovery in time series data generated by IoT

The problem of mining frequent patterns in non-temporal databases is studied extensively. Conventional frequent pattern algorithms are not applicable to find temporal frequent items from the temporal databases. Given a reference support time sequence, the problem of mining similar temporal association patterns has been the current interest among the researchers working in the area of temporal databases. The main objective of this research is to propose and validate the suitability of Gaussian distribution based dissimilarity measure to find similar and dissimilar temporal association patterns of interest. The measure designed serves as similarity measure for finding the similar temporal association patterns. Finally, in this research, we consider the problem of mining similarity profiled temporal patterns from the set of time stamped transaction of temporal database using proposed measure. We show using a case study how the proposed dissimilarity measure may be used to find the temporal frequent patterns and compare the same with the work carried in the literature. The proposed measure has the fixed lower bound and upper bound values as 0 and 1 respectively which is its advantage as compared to Euclidean distance measure which has no fixed upper bound.

An Approach for Mining Similar Temporal Association Patterns in Single Database Scan

Temporal pattern mining is the recent research among researchers contributing in the areas of data mining, medical mining, spatial data mining, health informatics and gaining significant interest in Internet of things, one of the top ten fields of interest expected to rule the computing world in the next few years. Temporal pattern mining is a knowledge discovery process which concentrates on mining temporal databases for discovering hidden temporal information which includes finding temporal patterns, temporal and spatio- temporal association rules, performing temporal clustering and classification to name a few of them. In this paper, the major objective is to find temporally outlier patterns from the temporal database of disjoint time-stamped transactions in a single database scan without a need to scan the database multiple times. We aim to eliminate n-1 database scans, performed when we use conventional approach of finding temporal patterns. We demonstrate the approach using a case study. The results show that the proposed approach is computationally efficient which is essentially because of single scan performed.

ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function

Abstract Data generated from Sensors, IoT environment and many real time applications is mainly spatial, temporal, or spatio-temporal. Some of them include data generated from geospatial, geographical, medical, weather, finance and environmental applications. Such data objects changes over time. Conventional knowledge discovery techniques available do not address the need for analyzing such complex datasets and hence data analysis has become increasingly complex and challenging. Soft computing principles such as fuzzy logic, evolutionary and nature inspired computations may be applied to analyze dynamically varying data. Analyzing temporal trends of association patterns requires handling the temporal data, as prevalence values of temporal patterns are implicitly vectors. Finding Prevalence values of temporal association patterns and validating them for similarity using conventional approach increases the computational complexity. This makes it challenging as the conventional data mining algorithms do not address this need. In this research, we propose a novel approach for estimation of temporal association pattern prevalence values and a novel temporal fuzzy similarity measure which holds monotonicity to find similarity between any two temporal patterns. Experiments are performed considering naive, sequential, spamine and proposed approach. The results obtained show the proposed approach is promising and reduces computational complexity in terms of computing true prevalence and optimizing execution times.