Temporal Association Mining Research Articles

Research on Fuzzy Temporal Event Association Mining Model and Algorithm

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.

Challenge Paper: The Vision for Time Profiled Temporal Association Mining

research-article Share on Challenge Paper: The Vision for Time Profiled Temporal Association Mining Authors: Vangipuram Radhakrishna Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India 0000-0002-7373-6276Search about this author , Gali Suresh Reddy Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, IndiaSearch about this author , Puligadda Veereswara Kumar Department of Computer Science and Engineering, Acharya Institute of Technology, Hyderabad, Telangana state, India Department of Computer Science and Engineering, Acharya Institute of Technology, Hyderabad, Telangana state, IndiaSearch about this author , Vinjamuri Janaki Department of Computer Science and Engineering, Vaagdevi College of Engineering, Telangana state, India Department of Computer Science and Engineering, Vaagdevi College of Engineering, Telangana state, IndiaSearch about this author Authors Info & Claims Journal of Data and Information QualityVolume 13Issue 2June 2021 Article No.: 12pp 1–8https://doi.org/10.1145/3404198Online:13 May 2021Publication History 5citation102DownloadsMetricsTotal Citations5Total Downloads102Last 12 Months70Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Data Mining Technique for Temporal Association Mining using SPN-Sigmoid Neural Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

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.

A functional temporal association mining approach for screening potential drug–drug interactions from electronic patient databases

ABSTRACTAims: Drug–drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on spontaneous reports. These methods suffer from severe underreporting, incompleteness, and various bias. The aim of this study was to more effectively screen potential DDIs using patient electronic data and temporal association mining techniques. Methods: We focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms. We introduced innovative functional temporal association rules where the degree of temporal association between two events within a patient case was defined by a function. Results: Preliminary test results on two drug pairs (i.e., <benazepril, losartan> and <benazepril, insulin>) were classified into 260 clinically meaningful categories. These categories were evaluated by physicians and the results exhibited that all the potential DDIs were confined to top 20 of the 260 outcomes. Conclusions: Our methodology can be used to dramatically reduce a long list of association rules to a manageable list for further analysis and investigation by drug safety professionals.

Discovering Analytic Associate Rule Filtering on Multi-Dimensional Data Streams

Multidimensional data points discover structural and chronological association inside the data streams. The PaDSkyline framework in a distributed environment utilized intra group optimization and multi filtering technique for skyline query processing. Skyline query processes within each group of distributed data sets, but dynamic filtering point selection was not performed with cost effective system. Similarity- Profiled temporal Association MINing mEthod (SPAMINE) used reference time sequences and threshold value to filter the information from real world data. Different similarity models for filtering temporal patterns were not very effective for performing the phase shift in time. To attain minimal phase shift based cost effective filtering on multidimensional data stream, Analytic Associate Rule Filtering (AARF) mechanism is proposed in this study. The main objective of AARF is to identify the relationship between attributes on multidimensional data and to filter out the independent attributes from the data streams. Initially, analytic association rule uses the weight computing factor to identify relationship and make inferences while testing multidimensional samples. Secondly, with the help of the analyzed relationship, the AARF mechanism uses the attribute independent criterion to discard negligible weight from association rule. Finally, to filter the analytic association rule with specified phase shift time, the ‘if-then’ strategy is used in AARF mechanism. AARF mechanism has an ability to make an analytic filtering with minimal phase shift time on multidimensional test dataset. The minimal phase shift time reduces the execution time factor and attains cost effective filtering system. Experiment is conducted using Japanese vowel multidimensional data set extracted from UCI repository for measuring the factors such as the average precision level, execution time, filtering query traffic efficiency and true positive rate.

A temporal interestingness measure for drug interaction signal detection in post-marketing surveillance.

Drug-drug interactions (DDIs) can result in serious consequences, including death. Existing methods for identifying potential DDIs in post-marketing surveillance primarily rely on the FDA's (Food and Drug Administration) spontaneous reporting system. However, this system suffers from severe underreporting, which makes it difficult to timely collect enough valid cases for statistical analysis. In this paper, we study how to signal potential DDIs using patient electronic health data. Specifically, we focus on discovery of potential DDIs by analyzing the temporal relationships between the concurrent use of two drugs of interest and the occurrences of various symptoms using novel temporal association mining techniques we developed. A new interestingness measure called functional temporal interest was proposed to assess the degrees of temporal association between two drugs of interest and each symptom. The measure was employed to screen potential DDIs from 21,405 electronic patient cases retrieved from the Veterans Affairs Medical Center in Detroit, Michigan. The preliminary results indicate the usefulness of our method in finding potential DDIs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals.

Predicting intraday prices in stock market transactions using similarity profiled temporal associations

The primary intension of any investor in the stock market is to catch the market trends at an early stage and accordingly transact buy or sell at the right time. Though stock market data is convertible into some form of multiple time series, it is difficult to process, analyse and mine manually. Researchers have proposed several methods to predict the future price of the stocks. In this paper, we proposed a method to predict the intraday price of a stock using the historic data. Given the time stamped transactions, the stock data is mined for pattern records using similarity profiled temporal association mining with reference to a cut-off value and for forming a pattern database. Using the support value for different price gain and the opening price of the stock for the day, we extract all the significant pattern records from the pattern database. Using the current trend of the stock, we project the future prices from time to time for the day. Wipro stock data from 2005 to 2009 are used for experimental evaluation of our approach. Expected price for various days are agreed to an extent of 98% with actual transaction prices.

Predicting Student Academic Performance Using Temporal Association Mining

Predicting Student Academic Performance Using Temporal Association Mining

Temporal-Based Video Event Detection and Retrieval

The fast proliferation of video data archives has increased the need for automatic video content analysis and semantic video retrieval. Since temporal information is critical in conveying video content, in this chapter, an effective temporal-based event detection framework is proposed to support high-level video indexing and retrieval. The core is a temporal association mining process that systematically captures characteristic temporal patterns to help identify and define interesting events. This framework effectively tackles the challenges caused by loose video structure and class imbalance issues. One of the unique characteristics of this framework is that it offers strong generality and extensibility with the capability of exploring representative event patterns with little human interference. The temporal information and event detection results can then be input into our proposed distributed video retrieval system to support the high-level semantic querying, selective video browsing and event-based video retrieval.

Similarity-Profiled Temporal Association Mining

Given a time stamped transaction database and a user-defined reference sequence of interest over time, similarity-profiled temporal association mining discovers all associated item sets whose prevalence variations over time are similar to the reference sequence. The similar temporal association patterns can reveal interesting relationships of data items which co-occur with a particular event over time. Most works in temporal association mining have focused on capturing special temporal regulation patterns such as cyclic patterns and calendar scheme-based patterns. However, our model is flexible in representing interesting temporal patterns using a user-defined reference sequence. The dissimilarity degree of the sequence of support values of an item set to the reference sequence is used to capture how well its temporal prevalence variation matches the reference pattern. By exploiting interesting properties such as an envelope of support time sequence and a lower bounding distance for early pruning candidate item sets, we develop an algorithm for effectively mining similarity-profiled temporal association patterns. We prove the algorithm is correct and complete in the mining results and provide the computational analysis. Experimental results on real data as well as synthetic data show that the proposed algorithm is more efficient than a sequential method using a traditional support-pruning scheme.