Mbonsai: Application Package for Sequence Classification by Tree Methodology

Objectives: To investigate the Modified Inverse Weibull Distribution (MIWD) to determine its effectiveness in modelling real customer transaction data, which is often skewed and exhibits heavy-tailed characteristics. Methods: Parameters of the model were estimated using the Maximum Likelihood Estimation (MLE) technique. The model's performance was evaluated using goodness-of-fit measures, such as the log-likelihood (ℓ), Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and Kolmogorov–Smirnov (K–S) statistic. The probability density function (PDF), cumulative distribution function (CDF), hazard function, and survival function were derived analytically and visualized with Python to assess model behavior and data alignment. Findings: The MIWD demonstrated a better fit for the customer transaction data than its sub-models and other competing distributions. The estimated probability density function (PDF), cumulative distribution function (CDF), hazard, and survival functions highlighted the model's adaptability in representing both skewness and heavy-tailed characteristics in actual transaction data. Novelty: The main contribution of this study is the empirical validation of the four-parameter MIWD using actual customer transaction data, demonstrating its superiority over established sub-models based on various quantitative criteria. The findings provide compelling evidence that the MIWD is a robust and adaptable alternative for modelling skewed, heavy-tailed transaction and lifetime data, with practical applications in customer behavior analysis, reliability engineering, and risk modelling. Keywords: Modified Inverse Weibull Distribution, Maximum Likelihood Estimation, Customer transaction data, Parameters, Rayleigh distribution

In this exploratory study of integrated marketing communications (IMC), we bring together relational and transactional data, and sales and marketing views, to investigate whether firms can enhance their position in the market in terms of both marketing-oriented and sales-based performance benchmarks. This article examines, in a business-to-business services context, the relationships between collecting various types of customer transaction and relational data, customer data quality, and two types of business unit performance. The first type of performance is a more traditional sales-oriented metric of sales-oriented business growth, growth in sales, and net income. The second type is a less traditional marketing-oriented metric, called marketing-oriented customer performance, a summed measure of retention rate, share of wallet, lifetime customer value, and return on investment (ROl). An extensive review of different types of transaction and relational data produced two different categories of transaction and relationship information.

Market basket analysis (MBA) aims to discover purchasing patterns and item associations from customer transaction data. A major drawback of current techniques for MBA is a lack of quantitative metrics to measure the real value associated with basket items. This paper addresses this gap by deriving a practical game-theoretic measure for MBA based on the Shapley value of cooperative games, which we call Shapley value index for MBA (SIMBA). The SIMBA of an item represents the average revenue it earns, including its influence on the revenue earned from sales of other items. A significant challenge when applying Shapley value-inspired approaches in practical domains is the exponential complexity of Shapley value computation. However, for the MBA domain, we show that SIMBA admits a scalable exact computation method that does not require sampling or other approximations. Specifically, a characteristic function for the MBA game is constructed so that the transaction dataset input corresponds to the game’s Harsanyi dividends. The relationship between Harsanyi dividends and the Shapley value is then exploited to efficiently compute SIMBA. This approach scales linearly in the number of transactions, making SIMBA a feasible approach for quantitative MBA. SIMBA can be used to screen conventional MBA techniques, such as association rules, to identify significant rules based on the items’ cross-selling capacity. This combination of existing MBA methods and SIMBA will generate rules based not only on frequency of co-occurrence, but also on the significance of the items. We demonstrate the working of the algorithm by analyzing openly available transaction data from an online retail store. To the best of our knowledge, this is the first time Shapley value is used in this way to solve market basket analyses of a practical size.

The research aims to overcome the problem of managing customer data, room data, and transaction data manually at Hotel Fergael. The management of customer data, room data and transaction data that are processed manually, is overcome by building a Front Office Information System for hotel room reservations at Hotel Fergael. The results of the Front Office Information System research for hotel room reservations at the Fergael Hotel can be done quickly, effectively and efficiently at the receptionist's work time, making reports can also assist managers in seeing reservations that have or have occurred at the Fergael Hotel. The system is designed in the form of Visual Basic and uses a MySQL database. With this research, the receptionist can expedite the activities that run at the Fergael Hotel, especially room input, customer input, and room reservations that occur at the Fergael Hotel Pematangsiantar. Keywords: Information System, Visual Basic, MySQL, Reservation, Hotel

We suggest a simple yet effective and parameter-free feature construction process for time series classification. Our process is decomposed in three steps: (i) we transform original data into several simple representations; (ii) on each representation, we apply a coclustering method; (iii) we use coclustering results to build new features for time series. It results in a new transactional (i.e. object-attribute oriented) data set, made of time series identifiers described by features related to the various generated representations. We show that a Selective Naive Bayes classifier on this new data set is highly competitive when compared with state-of-the-art times series classification methods while highlighting interpretable and class relevant patterns.KeywordsNear NeighborData RepresentationDynamic Time WarpingMultiple RepresentationOptimal GridThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Transaction clustering has received attention in recent developments of data mining. Traditional clustering methods are not useful to solve this problem. Transaction data sets are different from the traditional data sets in their high dimensionality, sparsity and numerous outliers. We introduce a new efficient algorithm for transaction clustering. The proposed algorithm is based on a caucus, which is fine-partitioned demographic groups based on purchase features of customers. Due to the important role caucus plays, we also present a heuristic method of caucus generation with the use of entropy. Experiments on real and synthetic data sets show that our approach can achieve a better result than existed methods.

Predictors of Likelihood and Intensity of Past-Year Mental Health Service Use in an Active Canadian Military Sample

Financial fraud has emerged as one of the most critical challenges in the digital economy, with increasingly sophisticated attack strategies threatening the security of online transactions. This study examined the application of artificial intelligence (AI) and machine learning (ML) models for enhancing fraud detection accuracy and operational efficiency. Six algorithms—Logistic Regression, Decision Tree, Random Forest, XGBoost, Stacking Ensemble, and Graph Neural Network (GNN)—were evaluated using a large, imbalanced dataset of financial transactions. Model performance was assessed based on accuracy, precision, recall, F1-score, and AUC-ROC, with results demonstrating the superiority of ensemble-based and graph-oriented methods. The GNN achieved the highest performance, leveraging its ability to model complex relational structures in transactional data. Feature importance analysis via SHAP values indicated that transaction amount, frequency, and previous fraud history were the most influential predictors. Computational cost analysis revealed a trade-off between model complexity and inference latency, with lightweight models offering faster response times but lower detection capability. The findings suggested that integrating explainable AI and ensemble learning could significantly improve fraud detection while ensuring regulatory compliance and operational transparency. This research contributes to both academic literature and practical financial security systems by offering empirical evidence and actionable insights for the deployment of advanced fraud detection technologies.

Background: The highly competitive pharmaceutical industry necessitates a deep understanding of customer behaviour for businesses to achieve long-term success. Traditional methods of attracting customers, such as blanket discounts, interviews, or questionnaires, often prove ineffective as they lack personalisation and fail to address the specific needs of distinct customer groups. Customer Relationship Management (CRM) plays a crucial role in this regard. One of the key challenges within CRM is customer segmentation, which involves grouping customers with similar buying habits to develop targeted marketing strategies and foster stronger customer relationships. Objective: This study aimed to leverage CRM by segmenting Osotsala Pharmacy's customers based on their purchasing patterns, providing valuable insights that can inform the development of targeted marketing strategies, potentially fostering stronger customer relationships. Method: This study analysed six years of anonymised customer transaction data provided by Osotsala Pharmacy. Data cleaning and visualisation tools (JupyterLab and Tableau Public) were utilised to prepare the data for analysis and identify customer purchasing patterns by customer segmentation analysis using LRFMP model. This collaborative effort ensured data privacy and a clear understanding of customer behaviour. Result: Analysing six years of customer transaction data (1 December 2017, to 30 November 2023), comprising 372,124 transactions for 16,144 unique customers (12.4% based on transaction count), revealed key insights. Pre-pandemic (2018), member contribution to revenue was only 25.3% with a small member base (6.4%). However, a positive correlation emerged over time, supported by a doubling in member base (10.3% to 20.4%) and revenue share jumped from 38.7% (2020) to 69.0% in (2023). These trends suggest that membership programs can be a driver of profitability. Additionally, by analysing LRFMP variables, the study identified at least two customer groups: Loyalty (high contribution) and Borderline (declining contribution, requiring attention). It provides a valuable foundation for planning and implementing targeted marketing activities. Conclusion: Adopting a data-driven approach is indispensable for better understanding of our relationships with customers.

To stay competitive, plenty of data mining techniques have been introduced to help stores better understand consumers' behaviors. However, these studies are generally confined within the customer transaction data. Actually, another kind of ‘deep shopping data’, e.g. which and why goods receiving much attention are not purchased, offers much more valuable information to boost the product design. Unfortunately, these data are totally ignored in legacy systems. This paper introduces an innovative system, called TagBooth, to detect commodities' motion and further discover customers' behaviors, using COTS RFID devices. We first exploit the motion of tagged commodities by leveraging physical-layer information, like phase and RSS, and then design a comprehensive solution to recognize customers' actions. The system has been tested extensively in the lab environment and used for half a year in real retail store. As a result, TagBooth generally performs well to acquire deep shopping data with high accuracy.

This research aims to explore the application of the Apriori algorithm in identifying purchasing patterns in the drop-off service industry in order to increase sales. Drop-off services often face challenges in designing effective marketing strategies due to limited understanding of customer purchasing behavior. In this study, the Apriori algorithm is applied to uncover recurring purchase patterns among customers, which are then used to develop more efficient marketing strategies. Customer transaction data is analyzed to find associations that reflect their purchasing preferences. The results show that the application of the Apriori algorithm successfully identifies patterns that can improve marketing strategies and, ultimately, increase sales. This research emphasizes the importance of applying data mining techniques to improve the performance of delivery services.

Homeowner preferences after September 11th, a microdata approach

Recommender system is the system which, by using automated information filtering technology, recommends products or services to the customers who are likely to be interested in. Those systems are widely used in many different Web retailers such as Amazon.com, Netfix.com, and CDNow.com. Various recommender systems have been developed. Among them, Collaborative Filtering (CF) has been known as the most successful and commonly used approach. CF identifies customers whose tastes are similar to those of a given customer, and recommends items those customers have liked in the past. Numerous CF algorithms have been developed to increase the performance of recommender systems. However, the relative performances of CF algorithms are known to be domain and data dependent. It is very time-consuming and expensive to implement and launce a CF recommender system, and also the system unsuited for the given domain provides customers with poor quality recommendations that make them easily annoyed. Therefore, predicting in advance whether the performance of CF recommender system is acceptable or not is practically important and needed. In this study, we propose a decision making guideline which helps decide whether CF is adoptable for a given application with certain transaction data characteristics. Several previous studies reported that sparsity, gray sheep, cold-start, coverage, and serendipity could affect the performance of CF, but the theoretical and empirical justification of such factors is lacking. Recently there are many studies paying attention to Social Network Analysis (SNA) as a method to analyze social relationships among people. SNA is a method to measure and visualize the linkage structure and status focusing on interaction among objects within communication group. CF analyzes the similarity among previous ratings or purchases of each customer, finds the relationships among the customers who have similarities, and then uses the relationships for recommendations. Thus CF can be modeled as a social network in which customers are nodes and purchase relationships between customers are links. Under the assumption that SNA could facilitate an exploration of the topological properties of the network structure that are implicit in transaction data for CF recommendations, we focus on density, clustering coefficient, and centralization which are ones of the most commonly used measures to capture topological properties of the social network structure. While network density, expressed as a proportion of the maximum possible number of links, captures the density of the whole network, the clustering coefficient captures the degree to which the overall network contains localized pockets of dense connectivity. Centralization reflects the extent to which connections are concentrated in a small number of nodes rather than distributed equally among all nodes. We explore how these SNA measures affect the performance of CF performance and how they interact to each other. Our experiments used sales transaction data from H department store, one of the well?known department stores in Korea. Total 396 data set were sampled to construct various types of social networks. The dependant variable measuring process consists of three steps; analysis of customer similarities, construction of a social network, and analysis of social network patterns. We used UCINET 6.0 for SNA. The experiments conducted the 3-way ANOVA which employs three SNA measures as dependant variables, and the recommendation accuracy measured by F1-measure as an independent variable. The experiments report that 1) each of three SNA measures affects the recommendation accuracy, 2) the density's effect to the performance overrides those of clustering coefficient and centralization (i.e., CF adoption is not a good decision if the density is low), and 3) however though the density is low, the performance of CF is comparatively good when the clustering coefficient is low. We expect that these experiment results help firms decide whether CF recommender system is adoptable for their business domain with certain transaction data characteristics.

In this paper, we propose an incremental clustering algorithm called INCLUS for high dimensional sparse transactional data using a newly defined similarity measure and a notion of cluster representatives based on locally frequent items of each cluster. INCLUS seeks structures in transactional data with respect to the support and similarity constraints specified by the users. The effectiveness and the order-independence property of INCLUS are empirically studied and compared with two state- of-art algorithms. Though it is a one-pass algorithm without any iterative refinement, INCLUS is not only effective and scalable, but also insensitive to the order of transactions, which is a crucial property for an incremental algorithm. globally frequent items are neglected during clustering, clusters embedded in such transactions cannot be discovered. In this paper, we define a new notion of cluster representative based on frequent items within each cluster that is easy to comprehend by users, and introduce a new similarity measure especially suitable for sparse transactional data. Using these, an incremental clustering algorithm INCLUS is proposed to cope with high cardinality of transactional datasets. INCLUS is a structure seeking algorithm as it produces clusters based on the support and similarity constraints specified by the users. The domains of input parameters (support and similarity thresholds) are well defined and can be easily chosen by users. INCLUS is significantly different from LargeItem, SUMMARY and TrK-Means which also employ frequent items for clustering. LargeItem uses global optimization to minimize both overlapping of frequent items among clusters and the number of infrequent items for a clustering. INCLUS uses frequent items for local optimization such that similarities for a clustering are maximized with respect to the similarity constraints. In SUMMARY, the frequent items used are globally frequent in the whole data set while in INCLUS they are only locally frequent within a cluster. Unlike INCLUS, TrK-Means does not use similarity constraints and it produces k or more clusters based on the user input of k. Squeezer (7) is an incremental clustering algorithm for categorical data. It uses a different similarity function from INCLUS, which is defined as the number of matched attribute values in a pair of tuples.

The robust beauty of improper linear models in decision making.