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The feed rate is an important evaluation index of combine harvester performance. The quick identification of the amount of feed rate that enters the combine during harvesting is of great significance for the efficiency and operational quality of the combine harvester. To address this issue, this study proposes a feed rate discrimination method based on association rule mining. A self-designed data acquisition system was designed, taking the wheat combine harvester as object, and collected seven speed signals and three torque signals when the feed rate was 6 kg/s-8 kg/s, 8 kg/s-10 kg/s, and 10 kg/s-11 kg/s, respectively. The collected time series data were discretized so as to facilitate the construction of transaction sets. Then, the association rules in the constructed transaction set were mined by FP-Growth, and the rules with weak or no correlation with the increase in feed rate were filtered using min-support, min-confidence, and min-lift of 1.3, 0.8, and 3, respectively, to obtain strong association rules. Then, the strong association rules were constructed as classifiers. The test results showed that the accuracy of the constructed classifier for the identification of 6 kg/s-8 kg/s, 8 kg/s-10 kg/s, and 10 kg/s-11 kg/s feed rates was 100 %, 96 %, and 98.7 %, respectively. Research results can provide a basis for the adjustment of the working state of the combine harvester. [ABSTRACT FROM AUTHOR]

The proposed work aims to solve data sparsity problem in the recommendation system. It handles two-level pre-processing techniques to reduce the data size at the item level. Additional resources like items genre, tag, and time are added to learn and analyse the behaviour of the user preferences in-depth. The advantage of the proposed method is to recommend the item, based on user interest pattern and avoid recommending the outdated items. User information are grouped based on similar item genre and tag feature. This effectively handle overlapping conditions that exist on item's genre, as it has more than one genre at initial level. Further, based on time, it analyses the user non-static interest. Overall it reduces the dimensions which is an initial way to prepare data, to analyse hidden pattern. To enhance the performance, the proposed method utilized Apache's spark Mllib FP-Growth and association rule mining approach in a distributed environment. To reduce the computation cost of constructing tree in FP-Growth, the candidate data set is stored in matrix form. The experiments were conducted using MovieLens data set. The observed results shows that the proposed method achieves 4% increase in accuracy when compared to earlier methods. [ABSTRACT FROM AUTHOR]

Meteorological disasters pose a significant challenge to megacities, and quantitatively identifying the influence of meteorological conditions on disasters is an urgent issue. Compared to other traditional datasets, disaster reporting data offers significant informational value for urban meteorological risk response and assessment due to its high credibility and timeliness. This study is based on meteorological disaster reporting data for Shanghai over the past 10 years and focuses on the identification and threshold analysis of meteorological disaster risk factors for disaster-affected entities, offering a novel perspective on analyzing disaster-causing risk under the combined influence of meteorological conditions. The findings suggest that disaster reporting data can be used for meteorological risk assessment. There were noticeable differences in the disaster thresholds for affected entities in central urban areas (city center) versus suburban areas (suburbs). The combined effects of strong winds and heavy rain were often significant contributors to the occurrence of disasters. Furthermore, several distinct disaster characteristics were identified through frequent pattern (FP)-growth mining. The results of this study provide technical support for early warning and disaster management under specific weather conditions by constructing a quantitative framework for understanding the impact of hazardous weather. • Disaster reporting data can provide valuable information for analyzing disaster impact characteristics. • A 10-year database composed of 51,409 disaster reporting data is used for analysis. • FP-growth algorithm extracted frequent patterns between meteorological conditions and disaster onset on affected entities. [ABSTRACT FROM AUTHOR]

In this research we extract knowledge from human resources data, accumulated in IT companies for the right selection of teams to work on software projects. We are looking for interesting and unknown dependencies and connections in the data, based on which managers can form more cohesive and professionally working project teams. The proposed approach to improve the selection of teams working on IT projects is based on association rule mining and can be used by IT managers to select the members of the teams. The approbation of the proposed approach is made using the software product RapidMiner. [ABSTRACT FROM AUTHOR]

Abstract In Electric Management Information System (MIS), there are some users who do not comply with all operation/behavior regulations and make the similar mistakes continuously even though they are not on purpose. These behaviors are a huge threat to the system security. In this paper, we propose a method to detect these regular behaviors with association rules mining algorithm FP-Growth. First, the user log is separated into operation sets each of which contains user operation in a continuous period. Then we divide the operation sets of all users into two catalogs: normal and abnormal based on if a security problem has happened around the corresponding period of operation set. Next, we apply the FP-Growth algorithm in both normal and abnormal operation sets to generate the frequent patterns. Finally, the abnormal pattern is compared with normal ones to determine the regular behaviors that may be dangerous to the system. We test the proposed algorithm in the user log files generated from a simulated electric management information system. The experiment results indicate the proposed method can effectively detect the regular user behavior that could cause the system security problems. [ABSTRACT FROM AUTHOR]

Identifying related offences in a criminal investigation is an important goal for crime analysts. This can deliver evidence that can assist in apprehension of suspects and better attribution of past crimes. The use of pattern based approaches has the potential to assist crime experts in discovering new patterns of criminal activity. Hence, research in this area continues. This paper revisits frequent pattern growth models for crime pattern mining. Frequent pattern (FP) based approaches, such as the FP-Growth model, have been identified to be more effective than techniques proposed in the past, such as Apriori. Therefore, this research proposes a descriptive statistical approach, based on a quartile (floor-ceil) function, for the minimum support threshold (MST) choice selection, which is a major decision step in the pruning phase of the Traditional FP-Growth (TFPG) model. Our revised frequent pattern growth (RFPG) model further proposes a Pattern-pattern ( Pp ) paradigm to identify tuples of subtle crime pattern(s) sequences or recurring trends in criminal activity. We present empirical results in order to guide intended audience about future decisions or research regarding this model. Results indicate that RFPG is more promising than TFPG and will always ensure the utilisation of a reasonable percentage of the crime dataset, in order to produce more reliable and sufficiently informative patterns or trends. [ABSTRACT FROM AUTHOR]

In contemporary internet architectures, including server farms and blog aggregators, web log data may be scattered among multiple cooperating peers. In order to perform content personalisation through provision of recommendations on such architectures, it is necessary to employ a recommendation algorithm; however, the majority of such algorithms are centralised, necessitating excessive data transfers and exhibiting performance issues when the number of users or the volume of data increase. In this paper, we propose an approach where the clickstream information is distributed to a number of peers, which cooperate for discovering frequent patterns and for generating recommendations, introducing: a) architectures that allow the distribution of both the content and the clickstream database to the participating peers; b) algorithms that allow collaborative decisions on the recommendations to the users, in the presence of scattered log information. The proposed approach may be employed in various domains, including digital libraries, social data, server farms and content distribution networks. [ABSTRACT FROM AUTHOR]

Abstract: FP-Growth is a recursive mining algorithm for frequent patterns. With each recursion, a new FP-tree and the corresponding header table are built. This paper proposes another approach to improve the mining efficiency: only one FP-tree is constructed, and instead of building new trees in every recursion, builds new header tables; frequent patterns are generated along the way, so we call this algorithm “header table recursion (HTR)” method. Tests show that it will get better performance for relative small datasets because the compress of the FP-tree has no significant impact on the performance yet inflicts extra time cost in tree creation operations. However on larger datasets, the traversal of the original FP-tree will become more time-consuming and its overall performance needs further testing. [Copyright &y& Elsevier]

Limitations existed with current methods for attack intention recognition. For instance, they lacked compensatory intrusion evidences, cost enormous system resources and bad low precision. To avoid the above flaws, a novel and effective method is proposed. The method generated compensatory intrusion evidences by fusing data from IDS and other security kits like scanner. Then, Bayesian-based attack scenarios were constructed where frequent attack patterns were identified using an efficient data-mining algorithm based on frequent patterns. Finally, attack paths were rebuilt by re-correlating frequent attack patterns mined in the scenarios to judge possible attack strategies precisely. The experimental results demonstrate the capability of the proposed method in rebuilding attack paths, recognizing attack intentions as well as in saving system resources. [ABSTRACT FROM AUTHOR]

Data Mining, also known as knowledge discovery in databases, has been recognized as a promising new area for database research. Mining frequent item sets in transactional databases, binary transaction tables, time series databases and many other kinds of databases have been an active research topic over the past few years. Frequent access pattern is a special case of sequential pattern in an application database which helps to make effective decisions in the respective problem domain. Given a large database of book transactions in the library, where each transaction consists of book-id, name of the book, author, and other related fields, the problem is to mine the frequent access patterns of the user from the library databases. The outcome of the findings will help the management to take effective steps that will cater the needs of the user. Apriori and FP-growth[5] algorithms can mine the complete sets of frequent item sets. These two algorithms were implemented and the performance of the algorithms was studied. The result shows that FP-growth algorithm performs well compared to Apriori. [ABSTRACT FROM AUTHOR]