Frequent Pattern Growth Algorithm Research Articles

Injury severity prediction and exploration of behavior-cause relationships in automotive crashes using natural language processing and extreme gradient boosting

Addressing the global challenge of traffic crashes necessitates transcending traditional statistical models, which often fail to fully capture the interactions between factors causing crashes. This oversight restricts the predictive accuracy and adaptability of current methodologies. Additionally, there is a notable gap in research that examines the links between behavior-cause relationships and crash injury severity. Our study deploys Natural Language Processing (NLP) and Frequent Pattern (FP) growth algorithm to mine crash narratives for behavior-cause connections, combines with the predictive strength of eXtreme Gradient Boosting (XGBoost) and the interpretative clarity offered by SHapley Additive exPlanations (SHAP), our approach not only predicts crash injury severity with satisfactory precision but also explains the influence of specific behavior-cause and environment conditions on crash outcomes. The integration of NLP and XGBoost, complemented by SHAP insights, has shown promising results with an accuracy of 0.79, outperforming traditional discrete choice models and competes closely with other machine learning approaches, including Support Vector Machines, Random Forest, Categorical Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM). Through detailed textual analysis and the establishment of a behavior-cause matrix, identifying five broad crash causes linked to 141 specific crash cause with behaviors, we uncover critical patterns such as the prominence of distracted driving in severe crashes. This comprehensive approach not only fills a critical research gap by linking behavior-cause relationships with injury severity but also sets the stage for developing targeted interventions to enhance road safety.

Inventory-based Business Strategy using FP-Growth at PT. Pinus Merah Abadi, Kendal Regency

This study aims to analyze inventory optimization using the Frequent Pattern Growth (FP Growth) algorithm, in relation to business strategy and/or accounting information systems, which a knowledge gap in previous research is as described above is the lack of knowledge about the application of the FP Growth algorithm in inventory optimization. This study was based on one year of sales and inventory data from a snack and beverage company, as a case study employing private data. A total of 3,370 transaction data samples were processed using RapidMiner data mining with FP growth algorithm. The results of both methods showed that the inventory level must be sufficient and produced two categories, namely large stock and small stock. The findings of this study indicated that the company has closely optimized its inventory since there were some products that were not involved in the association rules of sales.

Fuzzy association rule mining for personalized the Chinese language teaching from higher education

Chinese Language learning grows ever more essential to develop the students’ personalities and values as the curriculum, thereby improving teaching strategies based on students’ learning preferences are more crucial. Students’ participation in learning the Chinese Language is generally minimal and typically operates in a passive learning mode. The development of the Chinese Language instruction in these higher educational settings will be impacted by the absence of an organized strategy for teaching the Chinese Language. An algorithm is called the Fuzzy Pattern-driven Personalized Teaching (FPPT) has been proposed to identify the association between the students learning patterns and interests in the Chinese Language in the higher education for providing the personalized teaching to solve these challenges. Fuzzy sets are incorporated into FP-Growth for personalized the Chinese Language learning to improve the suggestions by considering the ambiguous preferences and the proficiency levels. The fuzzy pattern is unrevealed by implementing the Frequent Pattern (FP) growth algorithm to find patterns in the students learning activity and preferences so that personalized the teaching methods can be developed to meet the needs of each student and maximize their motivation for the language learning. Using the support and the confidence measures, these identified Fuzzy association relationships of student learning interest results in personalized the Chinese Language teaching in the higher education. The experimental results showed that the proposed FPPT system significantly improved each student’s learning outcome, communication effectiveness, learning motivation, and the Language proficiency level.

Finding optimum climatic parameters for high tomato yield in Benin (West Africa) using frequent pattern growth algorithm.

Tomato is one of the most appreciated vegetables in the world. Predicting its yield and optimizing its culture is important for global food security. This paper addresses the challenge of finding optimum climatic values for a high tomato yield. The Frequent Pattern Growth (FPG) algorithm was considered to establish the associations between six climate variables: minimum and maximum temperatures, maximum humidity, sunshine (Sun), rainfall, and evapotranspiration (ET), collected over 26 years in the three agro-ecological Zones of Benin. Monthly climate data were aggregated with yield data over the same period. After aggregation, the data were transformed into 'low', 'medium', and 'high' attributes using the threshold values defined. Then, the rules were generated using the minimum support set to 0.2 and the confidence to 0.8. Only the rules with the consequence 'high yield' were screened. The best yield patterns were observed in the Guinean Zone, followed by the Sudanian. The results indicated that high tomato yield was associated with low ET in all areas considered. Minimum and maximum temperatures, maximum humidity, and Sun were medium in every Zone. Moreover, rainfall was high in the Sudanian Zone, unlike the other regions where it remained medium. These results are useful in assessing climate variability's impact on tomato production. Thus, they can help farmers make informed decisions on cultivation practices to optimize production in a changing environment. In addition, the findings of this study can be considered in other regions and adapted to other crops.

Optimizing Inventory with Frequent Pattern Growth Algorithm for Small and Medium Enterprises

The success of a business heavily relies on its ability to compete and adapt to the ever-changing market dynamics, especially in the fiercely competitive retail sector. Amidst intensifying competition, retail business owners must strategically manage product placement and inventory to enhance customer service and meet consumer demand, considering the challenges of finding items. Poor inventory management often results in stock shortages or excess. To address this, adopting suitable inventory management techniques is crucial, including techniques from data mining, such as association rule mining. This research employed the FP-Growth algorithm to identify patterns in product placement and purchases, utilizing a dataset from clothing store sales. Analyzing 140 transactions revealed 24 association rules, comprising rules with 2-itemsets and frequently appearing 3-itemset rules. The highest support value in the final association rules with 2-itemsets was 10% with a confidence level of 56%, and the highest support value in the 3-itemsets was 67% with the same confidence level. Additionally, three rules had a confidence level of 100%. Thus, the association rules generated by the FP-Growth frequent itemset algorithm can serve as valuable decision support for sales of goods in small and medium-sized retail businesses.

Determining and Managing Stock of Goods Based on Purchasing Patterns Using the Frequent Pattern Growth Algorithm

Stock inventory is one of the important work activities, because stock inventory is the main element in the field of commerce. Mistakes regarding stock inventory will result in fatal problems, especially when stock inventory management is still done manually or improper stock inventory planning can cause the amount of stock to pile up due to the small amount of demand from consumers, resulting in the stock being damaged, especially those contains elements of expiration because it is not sold. On the other hand, if the stock of goods is low while consumer demand is high, consumers will turn to other supermarkets to look for the goods they want. This can make supermarkets lose money because they cannot meet consumer needs. Therefore, a system is needed to look for product item combination patterns using association techniques in determining and managing stock. This research aims to look for product item combination patterns in previous period transaction data based on purchasing patterns in determining and managing inventory at supermarkets using the FP-Growth method. Where with Min. Support 30% and Min. Confidence 70% produces 12 rules then with Min. Support 45% and Min.Confidence 60% produces 6 rules. Based on the comparison and analysis results obtained using the FP-Growth method, in this study the Min limit was chosen. Support 30% and Min. Confidence 70% due to generating 12 association rules. This information can be used as a reference for supermarkets in making decisions in determining and managing stock of goods based on purchasing patterns.

Market Basket Analysis of a Health Food Store in Thailand

This article presents a market basket analysis of a health food store in Thailand. The analysis identifies data attributes that frequently occur together in the dataset. Frequent occurrences of data attributes representing customer purchasing behaviors are extracted as association rules using the frequent pattern growth algorithm. The generated associations are evaluated using standard measures based on occurrence counts and an additional financial measure. Marketing strategies in the form of cross-selling pairs of specific products are then designed based on the data attributes appearing in the significant associations. The cross-selling products are offered at discounted prices and promoted in marketing campaigns. A break-even analysis is performed to estimate the required number of additional sales volumes from each marketing campaign to compensate for the discounted prices. The presented use case demonstrates the effectiveness of extending the market basket analysis to include a financial measure that can lead to practical marketing campaigns.

Application Of The Association Rule Method Based On Book Borrowing Patterns In Bojonegoro Regional Libraries

The library is an institution that processes collections of written and printed works, to meet the educational, research, information, and recreation needs of its users. The Bojonegoro Library Service provides reading materials with a collection of around 24,130 book titles and around 24,130 book copies. The number of registered visitors was 1,424 people. From 2021-2022, there are 303 book lending transaction data. Knowing the results of the Association Rule with the Frequent Pattem-Growth algorithm in determining recommendations for book placement based on borrowing patterns in libraries in the Bojonegoro area. The method used is Association Rule Mining, to produce an efficient algorithm, the algorithm used is the Frequent Pattern Growth (FP-Growth) Algorithm. The characteristic of the FP-Growth algorithm is the data structure used in a tree called FP-Tree. By using FP-Tree the FP-Growth algorithm can directly extract frequent itemsets from FP-Tree. The results of the research carried out by applying the FP growth algorithm with a support value limit of 20% and a confidence value of 80% from a dataset of 144 book lending transactions which became frequent itemsets were a combination of itemsets, resulting in a strong rule of 5 association rules which met the requirements. Can help the Bojongoro Library and archives service to improve the quality of service and can provide recommendations for librarians and as a reference for placing classes of books that are more often borrowed together closer together.

The lower He-sea points playing a significant role in postoperative ileus in colorectal cancer treated with acupuncture: based on machine-learning.

Postoperative ileus (POI) is a common complication following abdominal surgery, which can lead to significant negative impacts on patients' well-being and healthcare costs. However, the efficacy of current treatments is not satisfactory. The purpose of this study was to evaluate the therapeutic effects of acupuncture intervention and explore the regulation of acupoint selection for treating POI in colorectal cancer (CRC) patients. We searched eight electronic databases to identify randomized controlled trials (RCTs) on acupuncture for POI in CRC and conducted a meta-analysis. Subsequently, we utilized the Apriori algorithm and the Frequent pattern growth algorithm, in conjunction with complex network and cluster analysis, to identify association rules of acupoints. The meta-analysis showed that acupuncture led to significant reductions in time to first defecation (MD=-20.93, 95%CI: -25.35, -16.51; I2 = 93.0%; p < 0.01; n=2805), first flatus (MD=-15.08, 95%CI: -18.39, -11.76; I2 = 96%; p < 0.01; n=3284), and bowel sounds recovery (MD=-10.96, 95%CI: -14.20, -7.72; I2 = 94%; p < 0.01; n=2043). A subgroup analysis revealed that acupuncture not only reduced the duration of POI when administered alongside conventional care but also further expedited the recovery of gut function after colorectal surgery when integrated into the enhanced recovery after surgery (ERAS) pathway. The studies included in the analysis reported no instances of serious adverse events associated with acupuncture. We identified Zusanli (ST36), Shangjuxu (ST37), Neiguan (PC6), Sanyinjiao (SP6), Xiajuxu (ST39), Hegu (LI4), Tianshu (ST25), and Zhongwan (RN12) as primary acupoints for treating POI. Association rule mining suggested potential acupoint combinations including {ST37, ST39}≥{ST36}, {PC6, ST37}≥{ST36}, {SP6, ST37}≥{ST36}, and {ST25, ST37}≥{ST36}. Meta-analysis indicates acupuncture's safety and superior effectiveness over postoperative care alone in facilitating gastrointestinal recovery. Machine-learning approaches highlight the importance of the lower He-sea points, including Zusanli (ST36) and Shangjuxu (ST37), in treating POI in CRC patients. Incorporating additional acupoints such as Neiguan (PC6) (for pain and vomiting) and Sanyinjiao (SP6) (for abdominal distension and poor appetite) can optimize treatment outcomes. These findings offer valuable insights for refining treatment protocols in both clinical and experimental settings, ultimately enhancing patient care.

Student Identification Based on Patterns of Association For Student Misbehaviour Using Frequent Pattern Growth Algorithms

Student infractions are incidents often committed by students who break the rules at school. This naturally worries school authorities and overwhelms them with student misbehavior. Student rule-breaking is a common problem that can interfere with a safe and orderly learning environment. The more students break the rules, the greater the impact on several aspects, including student achievement, discipline, suboptimal teaching and learning activities, and students' social lives outside of school. Identifying students who are prone to rule violations can help school officials implement more effective prevention programs. Data mining is a process of extracting information from large data sets to discover patterns and relationships hidden within them. This study aims to identify frequent student infractions using the Frequent Pattern Growth algorithm. The Frequent Pattern Growth (FP -growth) algorithm is used to generate frequent itemsets that are then used in the association rules process. The association rules process aims to find rules or relationships between violations based on the discovered Frequent Itemsets. This process is influenced by predefined minimum support and minimum confidence values. A Minimum Support value of 30% and a Minimum Confidence value of 50% are used to obtain rules with a sufficiently high confidence level. It is expected that the identification results from this study will provide a better understanding of the types of violations commonly committed by students in school. This information can be used by school officials to develop more effective prevention strategies and focus on.

Implementasi Data Mining Pada Penjualan Pakaian dengan Algoritma FP-Growth

The amount of competition in the business world, especially in the sales industry, required developers to find a strategy that could increase sales and marketing of products sold, one of which was using clothing sales data with data mining. Data Mining was an iterative and interactive process to find new patterns or models that can be generalized for the future, valuable, and understandable in a massive database. HAS Stores in the arrangement of goods layout still place goods according to groups and types of goods, so that it has an impact on service and item search when consumers want to buy more than one item and are located far apart. Therefore, this study aims to apply the FP-Growth Algorithm to find out the most sold clothing sales at HAS Stores in Batam city. This study uses the Association Rule method by utilizing the FP-Growth Algorithm. This study aimed to apply the FP-Growth Algorithm to determine the most sold clothing sales at HAS Stores in Batam city. Through the mining process with the FP-Growth (Frequent Pattern Growth) algorithm, the types of clothes sold will be obtained, and how much inventory the store needs to provide the clothing stock. The results showed that the most sold clothing products were Gamis and Jilbab through the calculation of support 53,33% and confidence 100%. Regarding these results, marketing strategies can be focused on the product and set a layout that customers can easily see.

Association rule mining for prediction of COVID-19

COVID-19 is a raging pandemic that has created havoc with its impact ranging from loss of millions of human lives to social and economic disruptions of the entire world. The catastrophic shock of COVID-19 in India is also enormous. Currently, India has the largest number of COVID cases in Asia. Therefore, error-free prediction, quick diagnosis, disease identification, isolation and treatment of a COVID patient have become extremely important. Nowadays, mining knowledge and providing scientific decision making for diagnosis of diseases from clinical datasets has found wide-ranging applications in healthcare sector. In this direction, among different data mining tools, association rule mining has already emerged out as a popular technique to extract invaluable information and develop important knowledge-base to help in intelligent diagnosis of distinct diseases quickly and automatically. In this paper, an attempt is put forward to develop a predictive model based on frequent pattern growth algorithm of association rule mining to determine the likelihood of COVID-19 in a patient. It identifies breathing problem, fever, dry cough, sore throat, abroad travel and attended large gathering as the main indicators of COVID-19. Based on a large clinical dataset, a linear regression model is also proposed having an accuracy of 73.9% in correctly predicting the occurrence of COVID-19.

Effect of the chronic medication use on outcome measures of hospitalized COVID-19 patients: Evidence from big data.

Concerns about the role of chronically used medications in the clinical outcomes of the coronavirus disease 2019 (COVID-19) have remarkable potential for the breakdown of non-communicable diseases (NCDs) management by imposing ambivalence toward medication continuation. This study aimed to investigate the association of single or combinations of chronically used medications in NCDs with clinical outcomes of COVID-19. This retrospective study was conducted on the intersection of two databases, the Iranian COVID-19 registry and Iran Health Insurance Organization. The primary outcome was death due to COVID-19 hospitalization, and secondary outcomes included length of hospital stay, Intensive Care Unit (ICU) admission, and ventilation therapy. The Anatomical Therapeutic Chemical (ATC) classification system was used for medication grouping. The frequent pattern growth algorithm was utilized to investigate the effect of medication combinations on COVID-19 outcomes. Aspirin with chronic use in 10.8% of hospitalized COVID-19 patients was the most frequently used medication, followed by Atorvastatin (9.2%) and Losartan (8.0%). Adrenergics in combination with corticosteroids inhalants (ACIs) with an odds ratio (OR) of 0.79 (95% confidence interval: 0.68-0.92) were the most associated medications with less chance of ventilation therapy. Oxicams had the least OR of 0.80 (0.73-0.87) for COVID-19 death, followed by ACIs [0.85 (0.77-0.95)] and Biguanides [0.86 (0.82-0.91)]. The chronic use of most frequently used medications for NCDs management was not associated with poor COVID-19 outcomes. Thus, when indicated, physicians need to discourage patients with NCDs from discontinuing their medications for fear of possible adverse effects on COVID-19 prognosis.

A Novel Machine Learning Approach for Android Malware Detection Based on the Co-Existence of Features

This paper proposes a machine learning model based on the co-existence of static features for Android malware detection. The proposed model assumes that Android malware requests an abnormal set of co-existed permissions and APIs in comparing to those requested by benign applications. To prove this assumption, the paper created a new dataset of co-existed permissions and API calls at different levels of combinations, which are the second level, the third level, the fourth level and the fifth level. The extracted datasets of co-existed features at different levels were applied on permissions only, APIs only, permissions and APIs, and APIs and APIs frequencies. To extract the most relevant co-existed features, the frequent pattern growth (FP-growth) algorithm, which is an association rule mining technique, was used. The new datasets were extracted using Android APK samples from the Drebin, Malgenome and MalDroid2020 datasets. To evaluate the proposed model, several conventional machine learning algorithms were used. The results show that the model can successfully classify Android malware with a high accuracy using machine learning algorithms and the co-existence of features. Moreover, the results show that the achieved classification accuracy depends on the classifier and the type of co-existed features. The maximum accuracy, which is 98%, was achieved using the Random Forest algorithm and the co-existence of permissions features at the second combination level. Furthermore, the results show that the proposed approach outperforms the state-of-the-art model. Using Malgenome dataset, the proposed approach achieved an accuracy of about 98%, while the state-of-the-art achieved an accuracy of about 87%. In addition, the experiments show that using the Drebin dataset, the proposed approach achieved an accuracy of about 95%, while the state-of-the-art achieved an accuracy of about 93%.

How to implement a decision support for digital health: Insights from design science perspective for action research in tuberculosis detection

Association rule mining has gained much popularity in facilitating disease diagnosis and the healthcare industry's decision-making process. The cases of Drug Resistance Tuberculosis (DR-TB) have substantially increased in recent times and the factors driving its rapid rise have not been clearly identified. This research explores the Frequent Pattern (FP) Growth algorithm to identify recurring relationships, disease co-occurrences, and generate some very interesting diagnostics rules for DR-TB. This is achieved through data extraction from patients' TB health database, transforming raw data into a knowledge discovery system that provides a good idea for further exploration through medical research to clarify the unknown patterns from the obtained result. The FP growth algorithm efficiently generates the frequent symptoms associated with pulmonary tuberculosis and drug-resistance tuberculosis by generating the association rules at minimum confidence of 80%. The analysis performed is knowledgeable and conforms to the clinical practices and design science research processes.

On the causation correlation of maritime accidents based on data mining techniques

A great deal of valuable information included in maritime accident reports needs to be excavated to contribute to accident prevention or risk defence. In the present study, data mining technologies are applied to explore the potential causation correlations among the risk factors associated with maritime accidents. A collection of 285 accident reports is subjected to database analysis by using text mining technology to extract keywords, and the critical factors are then determined with reference to objective reports. The FP-Growth (frequent pattern growth) algorithm is then applied to identify the association rules hidden in the causations leading accidents, and the strength level of the identified association rules is evaluated quantitatively. The results show that the data mining technologies are applicable for identifying correlations hidden among factors contributing to maritime accidents. In addition, single factors do not significantly lead to accidents, while the integration of factors can easily cause accidents even under the condition of a good navigation environment. Therefore, stakeholders involved in maritime activities are advised to systematically assess risk factors, and prevent maritime accidents by interrupting the correlations among the factors.

Penerapan Algoritma FP-Growth dalam Penentuan Promosi Kampus

Campus or college is an institution as well as an institution that is included in the education sector. Now the campus is included in a business sector that is engaged in education. Therefore, the campus is also inseparable from the promotion process. The promotion carried out by the campus has the aim of making prospective new students register on the campus. For private campuses, students are input that can be useful to continue the operational process for the campus. The problem that is still widely found at this time is the process of determining campus promotions is still based on assumptions. Where the assumptions used are not based on a fact or special reference. Determination of promotion on campus can be seen based on the data of students who have registered actively on the campus. From the student data, it can be processed based on Regional Origin, School Origin, Department and Study Program taken. Data mining is a technique used to process large amounts of data. Frequent Pattern Growth Algorithm (FP-Growth) is an algorithm that is included in the data mining association technique. The results obtained in the study that for determining the promotion strategy are if you take the Study Program = Informatics Engineering (PS1) then the Department = Science (J1) and Regional Origin = Deli Serdang (AD1) with a Support value = 30% and a Confidence value = 60% and Department = Science (J1), Regional Origin = Deli Serdang (AD1) with Support = 30% and Confidence = 75%. Based on the results of the process that was carried out, it was found that the promotion target was Deli Serdang, the Department was Science and the Study Program of interest was Informatics Engineering.

Mining comorbidities of opioid use disorder from FDA adverse event reporting system and patient electronic health records

BackgroundOpioid use disorder (OUD) has become an urgent health problem. People with OUD often experience comorbid medical conditions. Systematical approaches to identifying co-occurring conditions of OUD can facilitate a deeper understanding of OUD mechanisms and drug discovery. This study presents an integrated approach combining data mining, network construction and ranking, and hypothesis-driven case–control studies using patient electronic health records (EHRs).MethodsFirst, we mined comorbidities from the US Food and Drug Administration Adverse Event Reporting System (FAERS) of 12 million unique case reports using frequent pattern-growth algorithm. The performance of OUD comorbidity mining was measured by precision and recall using manually curated known OUD comorbidities. We then constructed a disease comorbidity network using mined association rules and further prioritized OUD comorbidities. Last, novel OUD comorbidities were independently tested using EHRs of 75 million unique patients.ResultsThe OUD comorbidities from association rules mining achieves a precision of 38.7% and a recall of 78.2 Based on the mined rules, the global DCN was constructed with 1916 nodes and 32,175 edges. The network-based OUD ranking result shows that 43 of 55 known OUD comorbidities were in the first decile with a precision of 78.2%. Hypothyroidism and type 2 diabetes were two top-ranked novel OUD comorbidities identified by data mining and network ranking algorithms. Based on EHR-based case–control studies, we showed that patients with OUD had significantly increased risk for hyperthyroidism (AOR = 1.46, 95% CI 1.43–1.49, p value < 0.001), hypothyroidism (AOR = 1.45, 95% CI 1.42–1.48, p value < 0.001), type 2-diabetes (AOR = 1.28, 95% CI 1.26–1.29, p value < 0.001), compared with individuals without OUD.ConclusionOur study developed an integrated approach for identifying and validating novel OUD comorbidities from health records of 87 million unique patients (12 million for discovery and 75 million for validation), which can offer new opportunities for OUD mechanism understanding, drug discovery, and multi-component service delivery for co-occurring medical conditions among patients with OUD.

Mining Common Syntactic Patterns used by Java Programmers

Open source code repositories provide massive data as programs that have been used to develop different tools. These kinds of works have been included in the active Big Code and Mining Software Repositories research fields. Although different machine learning works already classify the syntactic constructs used by programmers, there are no reports about the most common syntactic patterns used by Java programmers. In this article, we describe a system we build to provide such a report. Our system retrieves the syntactic patterns used by Java programmers, distinguishing those utilized by experts and beginners. We also present the anomalies found in the usage of different syntactic constructs. We modify the OpenJDK compiler to double the syntactic information included in its Abstract Syntax Tree (AST), define a mechanism to translate ASTs into n-dimensional vectors, combine the information of different syntax constructs to build heterogeneous patterns, and apply the Frequent Pattern Growth algorithm to mine the syntactic patterns as association rules. The mined patterns allow expressing hierarchical subpatterns connected to one another, providing a high level of expressiveness.

An Optimization Model for Landscape Planning and Environmental Design of Smart Cities Based on Big Data Analysis

This paper adopts “digital landscape design logic,” analyzes and researches smart city and digital landscape design, and builds a digital city based on design logic, design basis, environment analysis, and results in a presentation based on the dilemma of landscape garden planning and design at this stage and the development trend of the smart garden and digital landscape design. The optimization model of the landscape and environment design is constructed based on design logic, design basis, environment analysis, and result presentation. First, on the Hadoop distributed computing platform based on the MapReduce parallel processing framework, we implement the massive small file processing methods (Hadoop Archives, CombineFileInputFormat, and Sequence Files) to compensate for the inherent defects of Hadoop and experimentally compare the memory consumption and execution efficiency of the three methods to propose a choice. The memory consumption and execution efficiency of the three methods are experimentally compared to propose a selection strategy. Finally, based on the MR-PFP algorithm, we parallelize the frequent itemset in-cab trajectory big data to generate interesting strong association rules. The experimental results show that the MR-PFP algorithm has better speedup ratio performance and higher mining efficiency than the parallel frequent pattern (PFP) growth algorithm. The research and analysis focused on the digital implementation of the standalone environmental analysis, using Rhino software and Grasshopper visual programming language to build parametric logic, establish parametric analysis models, and conduct a comprehensive analysis of the current environment. The study explores the use of digital landscape design methods and technologies in the landscape design process. Using Rhino + Grasshopper parametric and visualization programming software, we built parametric analysis models around elevation, slope, slope direction, water catchment, and viewable area and used mapping and overlay techniques to quantify the urban space. Finally, the purpose of collecting, monitoring, analyzing, simulating, creating, and reproducing landscape information is achieved.