Extracting hidden patterns from dates' product data using a machine learning technique

Abstract

<div style="’text-align: justify;">Mining in data is an important step for knowledge discovery, which leads to extract new patterns from datasets. It is a widespread methodology that has the capability to help ministries, companies, and experts for diving into the data to find important insights and patterns to help them take suitable decisions. The farmers and marketers of the date product in the production regions lack to discover the most important characteristics of dates types from the economically, healthy, and the type of consumers point of view to achieve the highest profits by choosing the best types and the most consumed. The research objective is to extract interesting patterns from the dates’ product dataset, using Machine Learning, based on association rules generation. This, in turn, will support the farmers, and marketers to discover new features related to the production, consumption, and marketing processes. This research used a real dataset collected from KSA, Qassim region, which is the first region of cultivation of palm, that produces the best types of dates in the Arab region. The data preprocessed and analyzed by the Apriori algorithm. The results show important features and insights related to the health benefits of dates, production, its consumption, consumers types, and marketing. Consequently, these results can be employed, for instance, to encourage individuals to consume dates for their nutritional value and their important health benefits., furthermore, the results encourage producers to focus on the production of preferable types and to improve the marketing policies of the other types.</div>

Students Performance: From Detection of Failures and Anomaly Cases to the Solutions-Based Mining Algorithms

“…For this reason, the association between data will be examined in three approaches by selecting two attributes in each method. All the rules generated will need to be higher than the minimum support value and also higher than the minimum confidence value [41]. For this reason, we determined the minimum support value to be 2%.…”

“…In contrast to this, one can confidently evaluate, to a degree of certainty, the discovered correlation, which is the probability that a transaction containing A also contains B [2]. The user identifies the initial values of minimum support and confidence to produce association rules so that when the generated rules with values of confidence and support for itemsets is lower than the predefined minimum value, these itemsets are not accepted as a frequent itemset; consequently, the generated rules will be rejected [30], [41], [42]. The Equation of support and confidence measures are given in Equation 9and Equation 10, respectively.…”

Prevalence of hypertension: predictive analytics review

“…Enterprises use the trends of data analytics and AI embedded in enterprise advanced application typically used in large organization to manage resources and customer information. There are five common prediction techniques that mostly used to build predictive model namely neural network, decision tree, linear regression, association rule mining and support vector machine (SVM) [8][9][10][11][12][13]. The description below explains briefly of each technique with the references paper that used the techniques.…”

“…In [10] apply the association rule with learning management system (LMS) data and present the rules and relevant results on its performance and suitability in LMS environment. Besides, [11] used association rules to extract pattern from the dates' of product dataset to support businesses to explore variety aspect related to productivity and process excellence. The results produce insights information on business vital sign, the its strategy related to consumer and marketing views.…”

Predictive analytics of university student intake using supervised methods

“…Afterall, this research is presenting data analytics and it is very important for Information Technology (IT) business around the world [25]. Nevertheless, analytics is not being widely implemented in universities, especially Malaysia and the implementation is expected to increase efficiency in enrolment process and gain knowledge from historical data [26]. Hence, the analytics is believed can help the university to achieve its goal to be a primary choice of potential applicants that is believed to accept the offer.…”