Frequent Item Research Articles

Multiuser Incomplete Preference K-Nearest Neighbor Query Method Based on Differential Privacy in Road Network

In view of the existing research in the field of k-nearest neighbor query in the road network, the incompleteness of the query user’s preference for data objects and the privacy protection of the query results are not considered, this paper proposes a multiuser incomplete preference k-nearest neighbor query algorithm based on differential privacy in the road network. The algorithm is divided into four parts; the first part proposes a multiuser incomplete preference completion algorithm based on association rules. The algorithm firstly uses the frequent pattern tree proposed in this paper to mine frequent item sets, then uses frequent item sets to mine strong correlation rules, and finally completes multiuser incomplete preference based on strong correlation rules. The second part proposes attribute preference weight coefficient based on multiuser’ s different preferences and clusters users accordingly. The third part compares the dominance of the query object, filters the data with low dominance, and performs a k-neighbor query. The fourth part proposes a privacy budget allocation method based on differential privacy technology. The method uses the Laplace mechanism to add noise to the result release and balance the privacy and availability of data. Theoretical research and experimental analysis show that the proposed method can better deal with the multiuser incomplete preference k-nearest neighbor query and privacy protection problems in the road network.

Incorporating user behavior flow for user risk assessment

PurposeInternet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.Design/methodology/approachMethod feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.FindingsBased on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.Originality/valueThis paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Market-Based Analysis: Apriori approach to analyze purchase patterns

In this paper, a data mining approach has been developed to analyze customer behavioral patterns and to get a further idea of the scale of the products purchased. Using the approach we found the best and least performing products, the average number of the products that the customer is interested in buying, And using the apriori algorithm we can generate the frequent items which can be bought together which will help us in suggesting products for the customer to buy. Each pattern is supported by the support and confidence generated using the apriori algorithm.

The application of big data technology in the predictive analysis of enterprise capital operation risk

The background of the big data era makes enterprise tax management face many opportunities and challenges, in order to improve the management of enterprise capital operation risks and promote the enterprise to take the road of sustainable development. This paper firstly indexes risk names with the help of web crawler technology, establishes data sources, and then circulates the crawler to obtain the required information. Secondly, a hashing algorithm is applied to compress the massive data into a unique and extremely compact section of hash values by means of constant mapping. Then association rules are used to determine the set of frequent risk items, and the values of the two are continuously changed to derive the final predictive analysis. Finally, a capital operation risk prediction and analysis platform is built by combining the above processes. In this paper, the effectiveness of the proposed platform is verified, and the practical results show that the accuracy of the proposed platform for risk prediction discovery is as high as 97%, and the time spent for risk discovery is controlled within 30 minutes. The relevant data results verify that big data technology improves the accuracy of enterprise capital operation risk prediction and analysis while accelerating the speed of risk discovery.

PREDIKSI PERMINTAAN MATA KULIAH SEMESTER ANTARA (PADAT) DENGAN MENGGUNAKAN ALGORITMA FOLD-GROWTH DAN FP-GROWTH

The intermediate semester is the lecture period which is held during the holidays between even and odd semesters which is one of the alternatives given to students. The main purpose of predicting the demand for intermediate semester courses is to be able to help students make improvements to less than optimal grades and to be able to shorten the lecture period. All forms of preparation, both schedules and lecturers' appointments made by the higher education institution are related to requests for courses to be followed by students. Frequent Pattern-Growth (FP-Growth) is an alternative algorithm that can be used to determine the most frequent data set (frequent item set) in a data set. The Fold-growth algorithm uses the SOTrieIT data structure to extract transaction patterns. The expected results in this study are to predict the courses that will be taken by students in the solid / intermediate semester so that the leadership can make good preparations.

The use of an alimentary index to assess anthropogenic debris on green turtles (Chelonia mydas)

Studies on novel methodologies addressed to quantification of anthropogenic marine debris (AMD) types ingested by green turtles (Chelonia mydas) is not common in the scientific literature. To fill this gap, we used the alimentary index (IAi) to quantify and classify AMD ingested by green turtles stranded in the Potiguar Basin, northeastern Brazil. We classified 295 green turtles as juveniles (JUV-I (n = 190) and JUV-II (n = 58)) and adults (ADU (n = 47)). We collected gut contents during necropsy and analyzed them using the IAi. The IAi values were categorized as main (IAi ≥ 0.5), secondary (0.25 ≤ IAi < 0.5), and accessory (IAi < 0.25). The results revealed that transparent soft plastic was an accessory item for the three size groups and the second most frequent item (IAi = 0.23) for JUV-I. Our study brings insights into the application of the IAi to evaluate the amount and types of AMD ingested by green turtles.

Together is Better: Heavy Hitters Quantile Estimation

Stream monitoring is fundamental in many data stream applications, such as financial data trackers, security, anomaly detection, and load balancing. In that respect, quantiles are of particular interest, as they often capture the user's utility. For example, if a video connection has high tail (e.g., 99'th percentile) latency, the perceived quality will suffer, even if the average and median latencies are low. In this work, we consider the problem of approximating the per-item quantiles. Elements in our stream are (ID, value) tuples, and we wish to track the quantiles for each ID. Existing quantile sketches are designed for a plain number stream (i.e., containing just a value). While one could allocate a separate sketch instance for each ID, this may require an infeasible amount of memory. Instead, we consider tracking the quantiles for the heavy hitters (most frequent items), which are often considered particularly important, without knowing them beforehand. We first present a couple of simple and effective algorithms that serve as baselines, a sampling approach and a sketching approach. Then, we present SQUAD, an algorithm that combines sampling and sketching while improving the asymptotic space complexity. Intuitively, SQUAD uses a background sampling process to capture the behaviour of the quantiles of an item before it is allocated with a sketch, thereby allowing us to use fewer samples and sketches. The algorithms are rigorously analyzed, and we demonstrate SQUAD's superiority using extensive~simulations on real-world traces.

LadderFilter: Filtering Infrequent Items with Small Memory and Time Overhead

Data stream processing is critical in streaming databases. Existing works pay a lot of attention to frequent items. To improve the accuracy for frequent items, existing solutions focus on accurately filtering infrequent items. While these solutions are effective, they keep track of all infrequent items and require multiple hash computations and memory accesses. This increases memory and time overhead. To reduce this overhead, we propose LadderFilter, which candiscard infrequent items efficiently in terms of both memory and time. To achieve memory efficiency, LadderFilter discards (approximately) infrequent items using multiple LRU queues. To achieve time efficiency, we leverage SIMD instructions to implement LRU policy without timestamps. We apply LadderFilter to four types of sketches. Our experimental results show that LadderFilter improves the accuracy by up to 60.6×, and the throughput by up to 1.37×, and can maintain high accuracy with small memory usage. All related code is provided open-source at Github.

Double-Anonymous Sketch: Achieving Top-K-fairness for Finding Global Top-K Frequent Items

Finding top-K frequent items has been a hot topic in data stream processing in recent years, which has a wide range of applications. However, most of existing sketch algorithms focuses on finding local top-K in a single data stream. In this paper, we work on finding global top-K in multiple disjoint data streams. We find that directly deploying prior sketch algorithms is often unfair under global scenarios, which will degrade the accuracy of global top-K. We define top-K-fairness and show that it is important for finding global top-K. To achieve top-K-fairness, we propose a new sketch framework, called the Double-Anonymous sketch. The process of finding global top-K items is similar to that of paper reviewing and democratic elections. In these scenarios, double-anonymity is often an effective strategy to achieve top-K-fairness. We also propose two techniques, hot panning, and early freezing, to further improve the accuracy. We theoretically prove that the Double-Anonymous sketch achieves top-K-fairnesswhile keeping high accuracy. We perform extensive experiments to verify top-K-fairness in the scenario of disjoint data streams. The experimental results show that the Double-Anonymous sketch's error is up to 129 times (60 times on average) smaller than the state-of-the-art. All the related source code is open-sourced and available at Github.

A trinocular view of the auxiliary verb will in COVID-19 briefings from Westminster and Holyrood

Abstract This corpus assisted investigation of the auxiliary verb will examines the various modalities and functions realised in the register of political briefings given by Scottish (Holyrood) and United Kingdom (Westminster) representatives throughout the first year of the coronavirus pandemic. A trinocular approach is taken to consider the lexicogrammatical environment of will (from around), the discourse semantics of will (from above) and the relationship between contraction and meaning (from below). Our trinocular approach with a focus on will as a highly frequent item with great meaning potential has enabled us to gain insights about the nature of how politicians used modality to persuade, organise and empathise within coronavirus media briefings and thus shape their public personas as leaders.

WOMDI-Apriori Data Mining Algorithm for Clustered Indicators Analysis of Specialty Groups in Higher Vocational Colleges

The cluster effect of specialty groups plays an important role in the development of Higher Vocational Colleges. The purpose of this research is to scientifically explore the interaction mech- anism of specialty groups clustering indexes in higher vocational colleges, uantitatively analyze the correlation of these indexes, nd explore reasonable measures to promote the specialty groups clustering effect in higher ocational olleges. Firstly, data denoising and field screening were car- ried out on the original data, and then the variables were clustered and divided into LHS (Left Hand Side) and RHS (Right Hand Side). Then, an improved multi-dimensional interactive Apri- ori association rule mining algorithm considering index weights and orientation constraints was proposed. The improved Apriori algorithm and the traditional Apriori algorithm were applied to mine the structured data sets. The results show that the improved WOMDI-Apriori algorithm in this study improves the accuracy by 79.96% compared with the traditional Apriori algorithm. The results indicate that, when the indicators of brand, key and characteristic majors at or above the provincial level, proportion of full-time teachers with double qualifications, and the number of internship students accepted by cooperative enterprises are at a low level, the number of projects and satisfaction proportion of employers with graduates would be negatively affected; The majr category of equipment manufacturing is subjected to various factors coupling, which may lead to different graduates’ counterpart mployment rate; for association rules where the uccessor of the mining results is dominated by negative results, measures should be taken to avoid or reduce the possibility of their occurrence as much as possible. For association rules in which the successors of the mining results are dominated by positive results, measures should be taken to facilitate the occurrence of these frequent item sets whenever possible. The framework proposed in this research can provide theoretical guidance for analyzing operating characteristics and promoting the positive effects of specialty groups in higher vocational colleges.

Monitoring marine litter on Funchal beaches (Madeira Island): Insights for litter management

Marine litter is a complex and multi-dimensional problem, with beach litter surveys being an important cost-effective tool for monitoring and assessing marine litter pollution. In Madeira Island (Portugal, NE Atlantic), a region awarded several times as ‘Europe’s Leading Island Destination’ and with a particular orography, there is a scarce understanding of the situation of marine debris. A two-year monitoring (July 2020 to April 2022) of macro-litter was conducted on two beaches in Funchal, the largest city and capital of the island. The abundance, composition, sources, and pathways of the stranded marine litter were assessed following OSPAR guidelines. During the two years, a total of 14,265 items were recorded. The mean litter density was found to be 0.29 items/m2 and the beaches’ clean index ranged between ‘very clean’ and ‘dirty’. Cigarette butts (30.9%) and plastic objects (30.7%) were the most frequent marine litter items, followed by paper/cardboard (9.2%) and metal items (8.3%). The composition of the litter showed that most of the monitored marine debris has a land-based source, with a strong contribution from smoking-related activities and littering in streams. By characterizing the waste and identifying its source, it was possible to propose integrated management actions at a local level. Outreach and raising awareness campaigns, together with actions to limit single-use plastic items and stimulate a circular economy, could contribute to limit marine litter on the studied island but also in other regions with similar profiles.

Fast Frequent Patterns Mining by Multiple Sampling With Tight Guarantee Under Bayesian Statistics.

Sampling from large dataset is commonly used in the frequent patterns (FPs) mining. To tightly and theoretically guarantee the quality of the FPs obtained from samples, current methods theoretically stabilize the supports of all the patterns in random samples, despite only FPs do matter, so they always overestimate the sample size. We propose an algorithm called multiple sampling-based FPs mining (MSFP). The MSFP first generates the set of approximate frequent items (AFI), and uses the AFI to form the set of approximate FPs without supports ( AFP*), where it does not stabilize the value of any item's or pattern's support, but only stabilizes the relationship ≥ or < between the support and the minimum support, so the MSFP can use small samples to successively obtain the AFI and AFP*, and can successively prune the patterns not contained by the AFI and not in the AFP*. Then, the MSFP introduces the Bayesian statistics to only stabilize the values of supports of AFP*'s patterns. If a pattern's support in the original dataset is unknown, the MSFP regards it as random, and keeps updating its distribution by its approximations obtained from the samples taken in the progressive sampling, so the error probability can be bound better. Furthermore, to reduce the I/O processes in the progressive sampling, the MSFP stores a large enough random sample in memory in advance. The experiments show that the MSFP is reliable and efficient.

On the Utility of Parents' Historical Data to Investigate the Causes of Autism Spectrum Disorder: A Data Mining-Based Framework

ObjectiveAutism Spectrum Disorder (ASD) is acknowledged as a challenge that influences the learning ability of adolescents and also negatively impacts their families. Autism may be caused due to environmental exposure or genetically inherited disorder, however, no definitive or universally customary reasons are known. This makes the issue fairly challenging. Material and methodsThis work focuses on identifying the reasons of ASD utilizing computational methods. For this, data is collected that focuses on parental history for finding the trigged features by reviewing antenatal, perinatal, and infant hazard factors of ASD. Afterwards, ML techniques are applied on the collected instances to develop a predictive model and identify the reasons to ASD. While collecting the data, samples are obtained for ASD and non-ASD individuals both. A total of 115 features are obtained from each subject. The collected dataset has 47% samples of the subjects with ASD. Dimensionality reduction, and four feature selection methods are applied on the data to eliminate noise and least valued features. The data is verified using two clustering techniques, i.e., k-means and k-medoid. To validate the clustering results five clustering validation indices are used. Later, three classifiers, i.e. k-nearest neighbor (k-NN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) are trained to predict cases with ASD. The frequent items mining technique and the descriptive analysis of the clustered data are utilized to identify the factors that may cause ASD. ResultsThe proposed framework enables to identify the features that may contribute towards ASD. Whereas, for the classification part, SVM classifier performs better than others do with an average accuracy of 98.34% in predicting the ASD cases. ConclusionThe results identified stress as the dominant feature and environmental factors, like frequent use of canned food and plastic/steel bottles during fertilization period that may contribute towards ASD.

A Recommendation System in E-Commerce with Profit-Support Fuzzy Association Rule Mining (P-FARM)

E-commerce is snowballing with advancements in technology, and as a result, understanding complex transactional data has become increasingly important. To keep customers engaged, e-commerce systems need to have practical product recommendations. Some studies have focused on finding the most frequent items to recommend to customers. However, this approach fails to consider profitability, a crucial aspect for companies. From the researcher’s perspective, this study introduces a novel method called Profit-supported Association Rule Mining with Fuzzy Theory (P-FARM), which goes beyond just recommending frequent items and considers a company’s profit while making product suggestions. P-FARM is an advanced data mining technique that creates association rules by finding the most profitable items in frequent item sets. From the practitioners’ standpoints, this method helps companies make better decisions by providing them with more profitable products with fewer rules. The results of this study show that P-FARM can be a powerful tool for improving e-commerce sales and maximizing profit for businesses.

A New Approach for Optimizing the Extraction of Association Rules

Association rule methods are among the most used approaches for Knowledge Discovery in Databases (KDD), as they allow discovering and extracting hidden meaningful relationships between attributes or items in large datasets in the form of rules. Algorithms to extract these rules require considerable time and large memory spaces. This paper presents an algorithm that decomposes this complex problem into subproblems and processes items by category according to their support. Very frequent items and fairly frequent items are studied together. To evaluate the performance of the proposed algorithm, it was compared with Eclat and LCMFreq on two actual transactional databases. The experimental results showed that the proposed algorithm was faster in execution time and demonstrated its efficiency in memory consumption.

Design of Computer-Aided Assessment and Recognition Platform for College Students' Innovative Training Plan with Data Matrix Mining

In this paper, an improved association rule algorithm based on vector product is used. This algorithm only needs to scan the transaction database once and does not generate a large number of frequent item candidate sets. The algorithm first maps the set of data transactions into a matrix of vectors, each column vector representing an attribute. Matrix factorization algorithms usually decompose the original data matrix into two or more low-dimensional matrices. This paper analyzes the common problems existing in society, colleges, and families in labor education, and innovatively proposes a four-dimensional comprehensive evaluation index system for labor education in colleges and universities, including “subject-dominated colleges—family education support—social environment support—college students’ individual characteristics”.

Association rules and prediction of transportation mode choice: Application to national travel survey data

Predicting transportation mode choice is a classic challenge of travel behavior research. Over the years, different theoretical concepts and modeling approaches have been applied. This paper elaborates the application of class association rules (CARs) and examines their predictive performance using data extracted from the 2015 National Dutch Travel Survey. To solve the problem how to activate rules that have high confidence but low support, the information gain (IG) concept is introduced in the model building process. The modeling process in this study first involves extracting frequent items from the data using the FP-Growth algorithm and deriving CARs from these frequent items. Next, the IG statistic is used to construct a novel model (named CARIG), which consists of a set of decision rules that formally represent behavioral scripts, for predicting individuals’ transportation mode choice. The performance of CARIG is compared with the performance of conventional class-based association rules (CBA), decision trees (DT), a convolutional neural network (CNN) and a logistic regression (LR) model. In addition, a 10-fold cross validation test using a grid search parameter optimization method is conducted to validate the proposed approach. The results show that the proposed method is promising in predicting transportation mode choices observed in the national travel survey data.

Prevalence of frailty and its association with oral hypofunction in older adults: a gender perspective

BackgroundPrevious studies have indicated an association between oral hypofunction and frailty in community-dwelling older adults. However, this issue has not been evaluated in institutionalized older patients. We aimed to determine the prevalence of physical frailty in this particularly vulnerable group and evaluate its association with oral hypofunction, analyzing possible differences by gender.MethodsThis cross-sectional study was conducted in private and public care homes in Guayaquil (Ecuador) from January 2018 until December 2019. Participants were classified as robust, pre-frail, and frail according to the Fried's frailty phenotype. Oral hypofunction was defined as the presence of at least three positive items in the following list: poor oral hygiene, oral dryness, reduced occlusal force, decreased masticatory function, and deterioration of swallowing function. The relationships between frailty and oral hypofunction were analyzed using logistic regression models for the whole sample and stratified by gender. Statistical analyses were performed using STATA 15.0 software (Stata Corp. LP, College Station, TX, USA).ResultsAmong the 589 participants analyzed (65% women), the median age was 72 years (interquartile range: 66–82). Pre-frailty and frailty were presented in 66.7% and 28.9% of them respectively. Weakness was the most frequent item (84.6%). There was a significant relationship between frailty and oral hypofunction in women. In the overall sample, the frequency of frailty was 2.06 times higher (95% CI 1.30–3.29) in patients with oral hypofunction, and this association was maintained in women (ORa: 2.18; 95% CI 1.21–3.94). Reduced occlusal force and decreased swallowing function were items significantly associated with the presence of frailty (ORa: 1.95; 95% CI 1.18–3.22 and ORa: 2.11; 95% CI 1.39–3.19, respectively).ConclusionThe prevalence of frailty and pre-frailty was high among institutionalized older people and was associated with the presence of hypofunction, especially in women. Decreased swallowing function was the most strongly item associated with frailty.

A method for assisting the accident consequence prediction and cause investigation in petrochemical industries based on natural language processing technology

Risk analysis for production processes in the petrochemical industry is an important procedure for consequence prediction and investigation of accidents. The analyzer must grasp the correlations between the possible causes and consequences. From the potential cause and effect found in risk analysis reports, complete clarification should be obtained. Therefore, this study presents a method for assisting accident consequence prediction and investigation in the petrochemical industry based on risk analysis reports using natural language processing technology. First, a hazard and operability (HAZOP) historical data table is established by filling over 7200 HAZOP analysis data points. Both the causes and consequences in the table are classified into 20 categories each using the Latent Dirichlet Allocation (LDA) models. The LDA clustering results are assigned classification for the cause and consequence topics to the cause and consequences of the HAZOP analysis data. Based on part-of-speech (POS) tagging, all the words in each cause and consequence record are divided into subject and action words. Next, the word combinations of subject and action words with a higher occurrence are considered the key phrases for describing and representing the corresponding cause and consequence topic classifications. The Apriori algorithm is used to determine the frequent item sets, acquire the association rules, and calculate the association degree to obtain the sort order; it can highlight general trends in relational cause and consequence topics. According to the results, the most likely cause of the consequence and the most likely consequence that the cause may lead to are identified. Finally, a visual interface is developed to present the data for the consequence prediction and cause investigation of accidents. The results reveal that the quantity and quality of historic data are important factors that may influence the results. This method can contribute to predicting the accident evolution trend of an abnormal situation, taking preventive measures in advance, improving the accuracy of early warning, and supporting emergency response measures.