An evolutionary model to mine high expected utility patterns from uncertain databases

In recent decades, mobile or the Internet of Thing (IoT) devices are dramatically increasing in many domains and applications. Thus, a massive amount of data is generated and produced. Those collected data contain a large amount of interesting information (i.e., interestingness, weight, frequency, or uncertainty), and most of the existing and generic algorithms in pattern mining only consider the single object and precise data to discover the required information. Meanwhile, since the collected information is huge, and it is necessary to discover meaningful and up-to-date information in a limit and particular time. In this paper, we consider both utility and uncertainty as the majority objects to efficiently mine the interesting high expected utility patterns (HEUPs) in a limit time based on the multi-objective evolutionary framework. The benefits of the designed model (called MOEA-HEUPM) can discover the valuable HEUPs without pre-defined threshold values (i.e., minimum utility and minimum uncertainty) in the uncertain environment. Two encoding methodologies are also considered in the developed MOEA-HEUPM to show its effectiveness. Based on the developed MOEA-HEUPM model, the set of non-dominated HEUPs can be discovered in a limit time for decision-making. Experiments are then conducted to show the effectiveness and efficiency of the designed MOEA-HEUPM model in terms of convergence, hypervolume and number of the discovered patterns compared to the generic approaches.acceptedVersio

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users\u27 queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy

Exploring Pattern Mining Algorithms for Hashtag Retrieval Problem

Hashtag is an iconic feature to retrieve the hot topics of discussion on Twitter or other social networks. This paper incorporates the pattern mining approaches to improve the accuracy of retrieving the relevant information and speeding up the search performance. A novel algorithm called PM-HR (Pattern Mining for Hashtag Retrieval) is designed to first transform the set of tweets into a transactional database by considering two different strategies (trivial and temporal). After that, the set of the relevant patterns is discovered, and then used as a knowledge-based system for finding the relevant tweets based on users' queries under the similarity search process. Extensive results are carried out on large and different tweet collections, and the proposed PM-HR outperforms the baseline hashtag retrieval approaches in terms of runtime, and it is very competitive in terms of accuracy.publishedVersio

Collaborative Planning and Event Monitoring Over Supply Chain Network

The shifting paradigm of supply chain management is manifesting increasing reliance on automated collaborative planning and event monitoring through information-bounded interaction across organizations. An end-to-end support for the course of actions is turning vital in faster incident response and proactive decision making. Many current platforms exhibit limitations to handle supply chain planning and monitoring in decentralized setting where participants may divide their responsibilities and share computational load of the solution generation. In this thesis, we investigate modeling and solution generation techniques for shared commodity delivery planning and event monitoring problems in a collaborative setting. In particular, we first elaborate a new model of Multi-Depot Vehicle Routing Problem (MDVRP) to jointly serve customer demands using multiple vehicles followed by a heuristic technique to search near-optimal solutions for such problem instances. Secondly, we propose two distributed mechanisms, namely: Passive Learning and Active Negotiation, to find near-optimal MDVRP solutions while executing the heuristic algorithm at the participant's side. Thirdly, we illustrate a collaboration mechanism to cost-effectively deploy execution monitors over supply chain network in order to collect in-field plan execution data. Finally, we describe a distributed approach to collaboratively monitor associations among recent events from an incoming stream of plan execution data. Experimental results over known datasets demonstrate the efficiency of the approaches to handle medium and large problem instances. The work has also produced considerable knowledge on the collaborative transportation planning and execution event monitoring

Exploring the Existing and Unknown Side Effects of Privacy Preserving Data Mining Algorithms

The data mining sanitization process involves converting the data by masking the sensitive data and then releasing it to public domain. During the sanitization process, side effects such as hiding failure, missing cost and artificial cost of the data were observed. Privacy Preserving Data Mining (PPDM) algorithms were developed for the sanitization process to overcome information loss and yet maintain data integrity. While these PPDM algorithms did provide benefits for privacy preservation, they also made sure to solve the side effects that occurred during the sanitization process. Many PPDM algorithms were developed to reduce these side effects. There are several PPDM algorithms created based on different PPDM techniques. However, previous studies have not explored or justified why non-traditional side effects were not given much importance. This study reported the findings of the side effects for the PPDM algorithms in a newly created web repository. The research methodology adopted for this study was Design Science Research (DSR). This research was conducted in four phases, which were as follows. The first phase addressed the characteristics, similarities, differences, and relationships of existing side effects. The next phase found the characteristics of non-traditional side effects. The third phase used the Privacy Preservation and Security Framework (PPSF) tool to test if non-traditional side effects occur in PPDM algorithms. This phase also attempted to find additional unknown side effects which have not been found in prior studies. PPDM algorithms considered were Greedy, POS2DT, SIF_IDF, cpGA2DT, pGA2DT, sGA2DT. PPDM techniques associated were anonymization, perturbation, randomization, condensation, heuristic, reconstruction, and cryptography. The final phase involved creating a new online web repository to report all the side effects found for the PPDM algorithms. A Web repository was created using full stack web development. AngularJS, Spring, Spring Boot and Hibernate frameworks were used to build the web application. The results of the study implied various PPDM algorithms and their side effects. Additionally, the relationship and impact that hiding failure, missing cost, and artificial cost have on each other was also understood. Interestingly, the side effects and their relationship with the type of data (sensitive or non-sensitive or new) was observed. As the web repository acts as a quick reference domain for PPDM algorithms. Developing, improving, inventing, and reporting PPDM algorithms is necessary. This study will influence researchers or organizations to report, use, reuse, or develop better PPDM algorithms

Predicate based association rules mining with new interestingness measure

Association Rule Mining (ARM) is one of the fundamental components in the field of data mining that discovers frequent itemsets and interesting relationships for predicting the associative and correlative behaviours for new data. However, traditional ARM techniques are based on support-confidence that discovers interesting association rules (ARs) using predefined minimum support (minsupp) and minimum confidence (minconf) threshold. In addition, traditional AR techniques only consider frequent items while ignoring rare ones. Thus, a new parameter-less predicated based ARM technique was proposed to address these limitations, which was enhanced to handle the frequent and rare items at the same time. Furthermore, a new interestingness measure, called g measure, was developed to select only highly interesting rules. In this proposed technique, interesting combinations were firstly selected by considering both the frequent and the rare items from a dataset. They were then mapped to the pseudo implications using predefined logical conditions. Later, inference rules were used to validate the pseudo-implications to discover rules within the set of mapped pseudo-implications. The resultant set of interesting rules was then referred to as the predicate based association rules. Zoo, breast cancer, and car evaluation datasets were used for conducting experiments. The results of the experiments were evaluated by its comparison with various classification techniques, traditional ARM technique and the coherent rule mining technique. The predicate-based rule mining approach gained an accuracy of 93.33%. In addition, the results of the g measure were compared with a state-of-the-art interestingness measure developed for a coherent rule mining technique called the h value. Predicate rules were discovered with an average confidence value of 0.754 for the zoo dataset and 0.949 for the breast cancer dataset, while the average confidence of the predicate rules found from the car evaluation dataset was 0.582. Results of this study showed that a set of interesting and highly reliable rules were discovered, including frequent, rare and negative association rules that have a higher confidence value. This research resulted in designing a methodology in rule mining which does not rely on the minsupp and minconf threshold. Also, a complete set of association rules are discovered by the proposed technique. Finally, the interestingness measure property for the selection of combinations from datasets makes it possible to reduce the exponential searching of the rules

Incremental Mining of Frequent Serial Episodes Considering Multiple Occurrences

International audienceThe need to analyze information from streams arises in a variety of applications. One of its fundamental research directions is to mine sequential patterns over data streams. Current studies mine series of items based on the presence of the pattern in transactions but pay no attention to the series of itemsets and their multiple occurrences. The pattern over a window of itemsets stream and their multiple occurrences, however, provides additional capability to recognize the essential characteristics of the patterns and the inter-relationships among them that are unidentifiable by the existing presence-based studies. In this paper, we study such a new sequential pattern mining problem and propose a corresponding sequential miner with novel strategies to prune the search space efficiently. Experiments on both real and synthetic data show the utility of our approach