Mining Outlier Temporal Association Patterns

Outlier temporal pattern mining problem is the study and discovery of abnormal, invalid, anomalous temporal patterns in a given temporal database. In this paper, we address the approach for mining of outlier temporal patterns with respect to a given threshold and reference. To verify if the given pattern is an outlier pattern, we compute the true support of temporal pattern and then obtain the distance between this pattern and reference temporal pattern using a novel measure. If the threshold distance computed using the proposed measure exceeds the minimum threshold limit, the pattern is treated as an outlier. Discovery and prediction of repeating temporal patterns and understanding the behavior of temporal pattern trends is quite challenging in the case of time stamped temporal datasets. At present, existing algorithms for temporal pattern mining do not have methods to reveal pattern which are emerging, seasonal and diminishing. Determining similar temporal patterns and unearthing eccentric patterns require an efficient dissimilarity measure. This research addresses the similarity measure for revealing outlier temporal patterns.

In this article, we propose a novel temporal pattern mining problem, named high-utility temporal pattern mining , to fulfill the needs of various applications. Different from classical temporal pattern mining aimed at discovering frequent temporal patterns, high-utility temporal pattern mining is to find each temporal pattern whose utility is greater than or equal to the minimum-utility threshold. To facilitate efficient high-utility temporal pattern mining, several extension and pruning strategies are proposed to reduce the search space. Algorithm HUTPMiner is then proposed to efficiently mine high-utility temporal patterns with the aid of the proposed extension and pruning strategies. Experimental results show that HUTPMiner is able to prune a large number of candidates, thereby achieving high mining efficiency.

Blast furnace (BF) is the main method of modern iron-making. Ensuring the stability of the BF conditions can effectively improve the quality and output of iron and steel. However, operations of BF depend on mainly human experience, which causes two problems: (1) human experience is not objective and is difficult to inherit and learn and (2) it is difficult to acquire knowledge that contains time information among multiple variables in BF. To address these problems, a data-driven method is proposed. In this article, we propose a novel and efficient algorithm for discovering underlying knowledge in the form of temporal association rules (TARs) in BF iron-making data. First, a new TAR mining framework is proposed for mining temporal frequent patterns. Then, a novel TAR mining algorithm is proposed for mining underlying, up-to-date, and effective knowledge in the form of TARs. Finally, considering the updating of the BF database, a rule updating method is proposed that is based on the algorithm that is proposed in this article. Our extensive experiments demonstrate the satisfactory performance of the proposed algorithm in discovering TARs in comparison with the state-of-the-art algorithms. Experiments on BF iron-making data have demonstrated the superior performance and practicability of the proposed method.

Incremental sequential patterns for multivariate temporal association rules mining

Early detection of adverse climatic conditions saves life and prevents harmful health impacts. One of the factors that causes such climatic conditions is the changes in ozone layer. Hyper levels of ozone concentration may cause several lung diseases and respiratory disorders. Many data mining algorithms perform analysis on the given data only after performing discretization. Discretisation involves the process of partitioning the data intervals of the attributes with crisp boundaries which leads to over estimation or under estimation of the boundary values. Fuzzy logic overcomes this problem by considering the natural boundaries of attributes. Genetic algorithm overcomes this crisp boundary problem by considering the entire interval of the attribute and yields optimal boundaries. Temporal data mining involves the process of extracting useful information from temporal database. The proposed method identifies these unusual climatic conditions using fuzzy logic and Genetic algorithm. Our method aims to find out the unexpected parameters(in terms of climatological variables) that causes ozone day/normal day from ozone level detection database which is of quantitative, multidimensional and time series in nature by using temporal outliers. These parameters which causes ozone day/normal day are identified by using Temporal Association Rule mining. Then the temporal association rules are pruned by the unexpectedness measure, Leverage to generate temporal outliers. Finally our proposed approach is validated through a set of experiments which show its efficiency in terms of accuracy.

Mining frequent temporal duration-based patterns on time interval sequential database

Today, with the proliferation of Internet of Things (IoT) applications in almost every area of our society comes the trouble of deducing relevant information from real-time time-series data (from different sources) for decision making. In this paper, we propose a fuzzy temporal approach for crossing such data sets with the ultimate goal of exploiting them for temporal gradual pattern mining. A temporal gradual pattern may take the form: “the higher the humidity, the lower the temperature, almost 15 min later”. In addition, we apply parallel processing on our implementation and measure its computational performance.

Large networks of sensors are used to detect intrusions and provide security at the borders of the United States. Sensor signals are used to detect possible intrusions such as illegal immigration traffic in drugs, weapons, and smuggled goods at specific targeted geographic locations. GIS systems can be used to capture, store and analyze this location based intervention data. Using a GIS system, a spatial database can be generated from the sensor intervention data which can take into account relevant geographic information in the vicinity of the sensed interventions. Important geographic features that are close to the intervention locations such as: plateaus, hills, valleys or roadways can be extracted and added to the analysis using ArcGIS. GIS techniques alone cannot reveal meaningful hidden information within geographic data. We have developed an integrated approach involving data mining and GIS techniques to extract patterns and trends in geographic data that can aid and inform analysis. Our approach uses both spatial and association data mining techniques. Spatial data mining is the process of discovering previously unknown, interesting and potentially useful patterns from spatial datasets. Applying association rule mining to the spatial data can reveal additional important spatial relationships and help determine the relevance and importance of the sensor data. Spatial association rule mining was used to discover patterns in the intervention data, such as linking a sensed intrusion with a potentially hidden location such as a canyon, to infer a high probability of illegal traffic or immigration.

ITUFP: A fast method for interactive mining of Top-K frequent patterns from uncertain data

Spatial data mining is a special type of data mining. The main difference between data mining and spatial data mining is that in spatial data mining tasks we use not only non-spatial attributes but also spatial attributes. Spatial data mining techniques have strong relationship with GIS (Geographical Information System) and are widely used in GIS for inferring association among spatial attributes, clustering and classifying information with respect to spatial attributes. In this paper we use the statistical package Weka on two models, which consist of two parcels plans from the Olt area of Romania. In our experimentation, we compare the results of the vector models depending on the values of the training datasets. Using these models with GIS data from the domain of Cadaster we analyze the performance of the Artificial Neural Networks in context of spatial data mining.

Uncertainties pervade spatial data mining. This paper proposes a method of spatial data mining handling randomness and fuzziness simultaneously. First, the uncertainties in spatial data mining are presented via characteristics, spatial data, knowledge discovery and knowledge representation. Second, the aspects of the uncertainties in spatial data mining are briefed. They often appear simultaneously, but most of the existing methods cannot deal with spatial data mining with more than one uncertainty. Third, cloud model is presented to mine spatial data with both randomness and fuzziness. It may also act as an uncertainty transition between a qualitative concept and its quantitative data, which is the basis of spatial data mining in the contexts of uncertainties. Finally, a case study on landslide-monitoring data mining is given. The results show that the proposed method can well deal with randomness and fuzziness during the process of spatial data mining.

Data mining is usually defined as searching,analyzing and sifting through large amounts of data to find relationships, patterns, or any significant statistical correlation. Spatial Data Mining (SDM) is the process of discovering interesting, useful, non-trivial patterns information or knowledge from large spatial datasets.Extracting interesting and useful patterns from spatial datasets must be more difficult than extracting the corresponding patterns from traditional numeric or categorical data due to the complexity of spatial data types, spatial relationships, and spatial auto-correlation.Emphasized overviewed the unique features that distinguish spatial data mining from classical Data Mining, and presents major accomplishments of spatial Data Mining research. Extracting interesting patterns and rules from spatial datasets, such as remotely sensed imagery and associated ground data, can be of importance in precision agriculture, community planning,resource discovery and other areas.

In GIS or Geographic Information system technology, a vast volume of spatial data has been accumulated, thereby incurring the necessity of spatial data mining techniques.Displaying and visualizing such data items are important aspects.But no RDBMS software is loaded with displaying the spatial result over a MAP overlay or answer spatial queries like "all the points within" certain Neighborhood.In this project, we propose a new spatial data mining system named SD-Miner.SD-Miner consists of three parts: A Graphical User Interface for inputs and outputs, a Data Mining Module that processes spatial data mining functionalities, a Data Storage Model that stores and manages spatial as well as non-spatial data by using a DBMS.In particular, the data mining module provides major spatial data mining functionalities such as spatial clustering, spatial classification, spatial characterization, and spatio-temporal association rule mining.SD-Miner has its own characteristics:(1) It supports users to perform non-spatial data mining functionalities as well as spatial data mining functionalities intuitively and effectively.(2) It provides users with spatial data mining functions as a form of libraries, thereby making applications conveniently use those functions.(3) It inputs parameters for mining as a form of database tables to increase flexibility.Result shows that significantly reduced and precise data items are displayed through the result of this technique.

Mining Temporal Patterns from temporal databases is challenging as it requires handling efficient database scan. A pattern is temporally similar when it satisfies subset constraints. The naive and apriori algorithm designed for non-temporal databases cannot be extended to find similar temporal patterns from temporal databases. The brute force approach requires computing \(2^n\) true support combinations for ‘n’ items from finite item set and falls in NP-class. The apriori or fp-tree based approaches are not directly extendable to temporal databases to obtain similar temporal patterns. In this present research, we come up with novel approach to discover temporal association patterns which are similar for pre-specified subset constraints, and substantially reduce support computations by defining expressions to estimate support bounds. The proposed approach eliminates computational overhead in finding similar temporal patterns. The results prove that the proposed method outperforms brute force approach.

A supermarket must have good business plan in order to meet customer desires. One way that can be done to meet customer desires is to find out the pattern of shopping purchases resulting from processing sales transaction data. Data processing produces information related to the function of the association between items of goods temporarily. Association rules functions in data mining.Association rule is one of the data mining techniques used to find patterns in combination of transaction data. Apriori algorithm can be used to find association rules. Apriori algorithm is used to find frequent itemset candidates who meet the support count. Frequent itemset that meets the support count is then processed using the temporal association rules method. The function of temporal association rules is as a time limitation in displaying the results of frequent itemsets and association rules. This study aims to produce rules from transaction data, apriori algorithm is used to form temporal association rules. The final results of this research are strong rules, they are rules that always appear in 3 years at certain time intervals with limitation on support and confidence, so that the rules can be used for business plan layout recommendations in Maharani Supermarket Demak.