Temporal Itemsets Research Articles

GANDIVA

In this research, the authors propose a novel tree structure called GANDIVA which computes true supports of all temporal itemsets by performing a tree-based scan and eliminating the database scan which is required for SPAMINE, G-SPAMINE, MASTER, and Z-SPAMINE approaches. The idea is to construct the tree called GANDIVA which determines support of all time-stamped temporal itemsets from the constructed tree. Another important advantage of the proposed approach is that it does not require the original database to be retained in the memory after a time profiled pattern tree (GANDIVA) is constructed from the original database. The significant advantage of GANDIVA over SPAMINE, G-SPAMINE, Z-SPAMINE, and MASTER is that GANDIVA requires zero database scans after the tree construction. GANDIVA is the pioneering research to propose a novel tree-based framework for seasonal temporal data mining.

On the application of sequential pattern mining primitives to process discovery: Overview, outlook and opportunity identification

AbstractSequential pattern mining (SPM) is a well‐studied theme in data mining, in which one aims to discover common sequences of item sets in a large corpus of temporal itemset data. Due to the sequential nature of data streams, supporting SPM in streaming environments is commonly studied in the area of data stream mining as well. On the other hand, stream‐based process discovery (PD), originating from the field of process mining, focusses on learning process models on the basis of online event data. In particular, the main goal of the models discovered is to describe the underlying generating process in an end‐to‐end fashion. As both SPM and PD use data that are comparable in nature, that is, both involve time‐stamped instances, one expects that techniques from the SPM domain are (partly) transferable to the PD domain. However, thus far, little work has been done in the intersection of the two fields. In this focus article, we therefore study the possible application of SPM techniques in the context of PD. We provide an overview of the two fields, covering their commonalities and differences, highlight the challenges of applying them, and, present an outlook and several avenues for future work.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Key Design Issues in Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining

Ultimate: Unearthing Latent Time Profiled Temporal Associations

Discovery of temporal association patterns, temporal association rules from temporal databases is extensively studied by academic research community and applied in various industrial applications. Temporal association pattern discovery is extended to similarity based temporal association pattern discovery from time-stamped transaction datasets by researchers Yoo and Sashi Sekhar. They introduced methods for pruning through distance bounds, and have also introduced SEQUENTIAL and SPAMINE algorithms for pattern mining that are based on snapshot data scan and lattice data scan strategies respectively. Our previous research introduced algorithms G-SPAMINE, MASTER, Z-SPAMINE for time profiled association pattern discovery. These algorithms applied distance measures SRIHASS, ASTRA, and KRISHNA SUDARSANA for similarity computations. SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, Z-SPAMINE approaches are all based on snapshot and lattice database scan strategies and prunes temporal itemsets by making use of lower bound, upper bound support time sequences and upper-lower distance bound, lower bound distance values. The major limitation of all these algorithms is their inevitability to eliminate dataset scanning process for knowing true supports of itemsets and essential need to have dataset available in memory. To eliminate the requirement of retaining dataset in main memory, algorithms VRKSHA and GANDIVA are two pioneering research contributions that introduced tree structure for time profiled temporal association mining. VRKSHA is based on snapshot tree scan technique while GANDIVA is a lattice tree scan based approach. VRKSHA and GANDIVA both apply Euclidean distance function, but they do not estimate support and distance bounds. This research introduces the pioneering work ULTIMATE that uses a novel tree structure. The tree is generated using similarity measure ASTRA. ULTIMATE uses support bound and distance bound computations for pruning temporal patterns. Experiment results showed that ULTIMATE outperforms SEQUENTIAL, SPAMINE, G-SPAMINE, MASTER, VRKSHA, GANDIVA algorithms.

VRKSHA: a novel tree structure for time-profiled temporal association mining

Mining association patterns from a time-stamped temporal database distributed over finite time slots is implicitly associated with task of scanning the input database. Finding supports of itemsets requires scanning the input database. Database scan can be performed in either snapshot or lattice-based approach. Sequential and SPAMINE methods for similarity-profiled association pattern mining originally proposed by Jin Soung Yoo and Sashi Sekhar are based on the snapshot database scan and lattice scan, respectively. Snapshot database scan involves scanning multi-time slot database time slot by time slot. The major limitation of Sequential method is the requirement to retain original temporal database in the disk for finding itemset support computations. In this paper, a novel multi-tree structure called VRKSHA is proposed that eliminates the need to store the original temporal database in the memory and also eliminates the need to retain database in memory. The basic idea is to generate a compressed time-stamped temporal tree and use this multi-tree structure to obtain true supports of temporal itemsets for a given time slot. Discovery of similar temporal itemsets is based on finding distance between temporal itemset and reference w.r.t each time slot and validating whether the computed distance satisfies specified user dissimilarity threshold. A pattern is pruned if the dissimilarity condition fails at any given time slot well before computing true support of itemset w.r.t all time slots. The advantage of proposed Sequential approach is from the fact that it is a single database scan approach excluding the initial database scan performed for computing true supports of singleton items. VRKSHA overcomes the major limitation of retaining database in memory that is required by SPAMINE, G-SPAMINE, MASTER algorithms. Experiment results prove that computational time and memory consumed by VRKSHA are significantly very much better than by approaches such as Naive, Sequential, SPAMINE, and G-SPAMINE. To the best of our survey and knowledge, VRKSHA is the pioneering work to introduce and propose a compressed tree-based data structure for mining similarity-profiled temporal association patterns in the area of time-profiled temporal association mining.

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

Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naive, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.

Mining Fuzzy Temporal Itemsets within Various Time Intervals in Quantitative Datasets

This research aims at proposing a new method for discovering frequent temporal itemsets in continuous subsets of a dataset with quantitative transactions. It is important to note that although these temporal itemsets may have relatively high textit{support} or occurrence within particular time intervals, they do not necessarily get similar textit{support} across the whole dataset, which makes it almost impossible to extract them using the existing traditional algorithms. This paper directly addresses this problem and introduces a new algorithm called Fuzzy Solid Linguistic Itemset Mining (FSLIM) to discover Solid Linguistic Itemsets (SLIs) within a quantitative dataset. SLI is a new concept introduced here as an essential part of the solution presented in this paper. The proposed method consists of two phases. In the first phase, fuzzy set theory is used to transform each quantitative value to a linguistic item; and in the second phase, all SLIs are extracted. Finally, the efficiency of FSLIM is compared in terms of execution time, scalability and the number of frequent patterns with those of two classic approaches on synthetic datasets. The proposed approach is also applied to an actual Mashhad Urban Traffic dataset in order to illustrate FSLIM's ability in discovering the hidden knowledge that could not be extracted by traditional methods.

Temporality and Context for detecting adverse drug reactions from longitudinal data

This paper introduces a method for mining co-occurring events from longitudinal data, and applies this method to detecting adverse drug reactions (ADRs) from patient data. Electronic health records are richer than older data sources (such as spontaneous report records) and thus are ideal for ADR mining. However, current data mining methods, such as disproportionality ratios and temporal itemset mining, ignore certain important aspects of the longitudinal data in patient records. In this paper, we highlight two specific problems with current methods, which we name temporal and contextual sensitivity, and discuss why these two properties are vital to mining patterns from longitudinal data. We also propose two sensitive longitudinal rate comparison measures, which utilize condition occurrence rates and length of drug eras, for mining ADRs from this type of data. These novel methods are then used to rank potential ADRs, along with existing state-of-the-art methods, under many simulated yet realistic datasets. In 48 out of 60 experiments, the proposed longitudinal rate comparison methods significantly outperform other methods in mining known ADRs from other drug / condition pairs.

Equipment Fault Prognosis Based on Temporal Association Rules

Equipment fault prognosis is important for reliability, operational safety, and efficient performance of equipment. Temporal fault data model is built according to the principles of the Apriori traditional association rules algorithm based on the characteristics of fault data. An Improved Apriori algorithm and frequent temporal association rules algorithm are proposed in this study by converting fault data to temporal item sets matrix. Equipment fault trends are predicted by mining the frequent temporal association rules of fault data based on the algorithm, which provides good support for equipment maintenance and management. At last an example is given to prove the feasibility and practical application of proposed algorithms. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4563 Full Text: PDF

Mining fuzzy temporal patterns from process instances with weighted temporal graphs

This paper presents an algorithm for mining fuzzy temporal patterns from a given process instance. The fuzzy representation of time intervals embedded between the activities is used for this purpose. Initially, the activities are portrayed with their temporal relationships through temporal graphs and then, the defined data structures are used to retrieve the data suitable for the proposed algorithm. Similar to the familiar k-itemsets and k-dim sequences, their counterparts are introduced in this work. The proposed process-instance level data structure generates an optimum number of temporal itemsets. The proposed algorithm differs from the other existing algorithms on this topic in the representation of the mined data and patterns. An example is provided to demonstrate the algorithm.

Twain

We investigate the general model of mining associations in a temporal database, where the exhibition periods of items are allowed to be different from one to another. The database is divided into partitions according to the time granularity imposed. Such temporal association rules allow us to observe short-term but interesting patterns that are absent when the whole range of the database is evaluated altogether. Prior work may omit some temporal association rules and thus have limited practicability. To remedy this and to give more precise frequent exhibition periods of frequent temporal itemsets, we devise an efficient algorithm Twain (standing for TWo end AssocIation miNer .) Twain not only generates frequent patterns with more precise frequent exhibition periods, but also discovers more interesting frequent patterns. Twain employs Start time and End time of each item to provide precise frequent exhibition period while progressively handling itemsets from one partition to another. Along with one scan of the database, Twain can generate frequent 2-itemsets directly according to the cumulative filtering threshold. Then, Twain adopts the scan reduction technique to generate all frequent k -itemsets ( k > 2) from the generated frequent 2-itemsets. Theoretical properties of Twain are derived as well in this article. The experimental results show that Twain outperforms the prior works in the quality of frequent patterns, execution time, I/O cost, CPU overhead and scalability.