Temporal Association Rule Mining Research Articles

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

Web usage mining with evolutionary extraction of temporal fuzzy association rules

In Web usage mining, fuzzy association rules that have a temporal property can provide useful knowledge about when associations occur. However, there is a problem with traditional temporal fuzzy association rule mining algorithms. Some rules occur at the intersection of fuzzy sets’ boundaries where there is less support (lower membership), so the rules are lost. A genetic algorithm (GA)-based solution is described that uses the flexible nature of the 2-tuple linguistic representation to discover rules that occur at the intersection of fuzzy set boundaries. The GA-based approach is enhanced from previous work by including a graph representation and an improved fitness function. A comparison of the GA-based approach with a traditional approach on real-world Web log data discovered rules that were lost with the traditional approach. The GA-based approach is recommended as complementary to existing algorithms, because it discovers extra rules.

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.

A Descriptive Framework for the Multidimensional Medical Data Mining and Representation

Problem statement: Association rule mining with fuzzy logic was explored by research for effective datamining and classification. Approach: It was used to find all the rules existing in the transactional database that satisfy some minimum support and minimum confidence constraints. Results: In this study, we propose new rule mining technique using fuzzy logic for mining medical data in order to understand and better serve the needs of Multidimensional Breast cancer Data applications. Conclusion: The main objective of multidimensional Medical data mining is to provide the end user with more useful and interesting patterns. Therefore, the main contribution of this study is the proposed and implementation of fuzzy temporal association rule mining algorithm to classify and detect breast cancer from the dataset.

IMTAR: Incremental Mining of General Temporal Association Rules

Nowadays due to the rapid advances in the field of information systems, transactional databases are being updated regularly and/or periodically. The knowledge discovered from these databases has to be maintained, and an incremental updating technique needs to be developed for maintaining the discovered association rules from these databases. The concept of Temporal Association Rules has been introduced to solve the problem of handling time series by including time expressions into association rules. In this paper we introduce a novel algorithm for Incremental Mining of General Temporal Association Rules (IMTAR) using an extended TFP-tree. The main benefits introduced by our algorithm are that it offers significant advantages in terms of storage and running time and it can handle the problem of mining general temporal association rules in incremental databases by building TFP-trees incrementally. It can be utilized and applied to real life application domains. We demonstrate our algorithm and its advantages in this paper.

An efficient algorithm for incremental mining of temporal association rules

This paper presents the concept of temporal association rules in order to solve the problem of handling time series by including time expressions into association rules. Actually, temporal databases are continually appended or updated so that the discovered rules need to be updated. Re-running the temporal mining algorithm every time is ineffective since it neglects the previously discovered rules, and repeats the work done previously. Furthermore, existing incremental mining techniques cannot deal with temporal association rules. In this paper, an incremental algorithm to maintain the temporal association rules in a transaction database is proposed. The algorithm benefits from the results of earlier mining to derive the final mining output. The experimental results on both the synthetic and the real dataset illustrate a significant improvement over the conventional approach of mining the entire updated database.

Identification of temporal association rules from time-series microarray data sets

BackgroundOne of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes. To elucidate the relationships between genes, an association rule mining (ARM) method has been applied to microarray gene expression data. However, a conventional ARM method has a limit on extracting temporal dependencies between gene expressions, though the temporal information is indispensable to discover underlying regulation mechanisms in biological pathways. In this paper, we propose a novel method, referred to as temporal association rule mining (TARM), which can extract temporal dependencies among related genes. A temporal association rule has the form [gene A↑, gene B↓] → (7 min) [gene C↑], which represents that high expression level of gene A and significant repression of gene B followed by significant expression of gene C after 7 minutes. The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.ResultsIn the parameter fitting phase of TARM, the fitted parameter set [threshold = ± 0.8, support ≥ 3 transactions, confidence ≥ 90%] with the best precision score for KEGG cell cycle pathway has been chosen for rule mining phase. With the fitted parameter set, numbers of temporal association rules with five transcriptional time delays (0, 7, 14, 21, 28 minutes) are extracted from gene expression data of 799 genes, which are pre-identified cell cycle relevant genes. From the extracted temporal association rules, associated genes, which play same role of biological processes within short transcriptional time delay and some temporal dependencies between genes with specific biological processes are identified.ConclusionIn this work, we proposed TARM, which is an applied form of conventional ARM. TARM showed higher precision score than Dynamic Bayesian network and Bayesian network. Advantages of TARM are that it tells us the size of transcriptional time delay between associated genes, activation and inhibition relationship between genes, and sets of co-regulators.

ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommodating temporal intervals offers rules that are richer still. In this paper we outline a new algorithm, ARMADA, to discover frequent temporal patterns and to generate richer interval-based temporal association rules. In addition, we introduce a maximum gap time constraint that can be used to get rid of insignificant patterns and rules so that the number of generated patterns and rules can be reduced. Synthetic datasets are utilized to assess the performance of the algorithm.