Data mining

Data streams are continuous flows of data objects generated at high rates, requiring real-time processing in a single pass. Clustering algorithms play a vital role in analyzing data streams by grouping similar data samples. Among various time windows for evolving streams, the sliding window method gradually moves over the data, focusing on the most recent information and improving clustering accuracy while reducing memory requirements. The development of distributed computing frameworks like Apache Spark has addressed the limitations of traditional tools in processing big data, including data streams. This paper presents the DynamicCluStream algorithm, an enhancement over Spark-CluStream, which employs a two-phase clustering approach with precise clustering of recent data. The algorithm dynamically determines the number of clusters by merging overlapping clusters during the offline phase, resulting in significant improvements in cluster precision. Experimental results show that it performs up to 47 percent better on average in terms of precision on the CoverType dataset and up to 92 percent better on average in terms of precision on the PowerSupply dataset.  Although the algorithm is slower due to data sample removal and cluster integration, its impact is negligible in a distributed environment.

The adoption of unhealthy lifestyles by individuals can lead to the development of various health conditions, including hypertension, high blood fat levels, and diabetes, posing significant risks to their well-being. This study focuses on examining the lifestyle of patients with diabetes and high blood fat levels in the city of "bordekhoon," conducted at Health Care Centers. Diabetes is a global health concern that is rapidly increasing and is associated with substantial costs. By applying data mining techniques, early detection of diabetes can be achieved, which can help prevent the progression of the disease and mitigate complications such as cardiovascular issues, vision problems, and kidney diseases. Nowadays, data mining-based approaches are employed to predict diabetes and hypertension, aiming to enhance early diagnosis accuracy and obtain valuable insights. In this paper, a combination of classification techniques (Ensemble Method) is used to predict and identify two types of diabetes. Factors such as gender, diet, fasting plasma glucose (FPG), physical activity, cigarette consumption, age, genetic predisposition, and body mass index (BMI) are modeled and analyzed using IBM SPSS Modeler 18 software. The accuracy of the employed techniques is ultimately presented.

Urban retail markets are state-owned retail markets that were recently established in Iran to increase the welfare of consumers and producers. To achieve this goal and expand its presence in the Iranian retail sector, it is essential to gain a comprehensive understanding of consumer behavior within these markets. This study examines the various socio-economic factors influencing consumers' decisions in the retail market by using the C4.5 algorithm. The data were collected using a random sampling method through a survey of 189 consumers, focusing on the population of Mashhad, Iran, during 2019-2020. Results revealed that awareness of available discounts significantly drives consumer choices in urban retail markets. Despite existing discounts, awareness among consumers remains low, suggesting a need to review promotional strategies within the marketing mix. The study also identifies previous purchases from urban markets, household income, and education as influential factors. Findings offer valuable insights for policymakers, market strategists, and stakeholders seeking to enhance the effectiveness of local retail markets in Iran. By leveraging insights into consumer behavior and market dynamics, these markets can thrive, benefiting Iran's retail sector and overall economy. Following the study, recommendations such as enhanced promotional campaigns, education-oriented strategies, loyalty programs, collaborations with local producers, and inclusive marketing policies was made aim to improve access for all consumers to urban retail markets.