Academic literature on the topic 'Apriori-like algorithm'

Data mining plays a vital role in converting the medical data like text, image, and graphs into meaningful new data, which helps to take the better decision. In this chapter, an overview of the current research is discussed using the data mining techniques for the finding, analysis, and prediction of various diseases. The focus of this study is to identify the well-performing data mining algorithms used on medical and clinical databases. Multiple algorithms have been identified: text-based mining, association rule-based mining, pattern-based mining, keyword-based mining, machine learning, neural network support vector machine, apriori algorithm, k-means clustering, and natural language. Analyses of the algorithm show that there is no single algorithm or model more suitable for diagnosing or predicting diseases. In some scenarios, some algorithms work very well but not in another data set. There are many examples in clinical or medical research where the combination of different algorithms gives good results.

Data processing technique of data mining has the power to identify patterns and relationships in huge volumes of data from multiple sources to make decisions to drive the world in the current scenario. Association rule mining (ARM) is the most significant method used in data mining. This approach is employed to find trends in the database that are typical. The field of ARM has seen a lot of activities. ARM remains a source of concern for various experts. There are algorithms that assess fundamental factors like precision, algorithm speed, and data assistance. The ARM algorithms, namely AprioriHybrid, AIS, AprioriTID, and Apriori, as well as FP-Growth, are examined in this work. This chapter provides a comparison of different algorithms utilized for association rules mining against several performance factors.

Movie streaming services are businesses driven by data and strategies to predict future viewing patterns based on historical data. Without unsupervised learning techniques, industries like movie streaming services might face laborious tasks and issues in anticipating customer preferences and forecasting changes in customer behavior. In this chapter, market basket analysis (MBA) and recommender systems were implemented on MovieLens Data. In MBA, movie watching patterns were identified using two types of rule-generating algorithms, namely the apriori algorithm and the FP-growth algorithm. Three visualization idioms were generated to understand the association rules extracted in the MBA. Secondly, five types of recommender systems, namely memory-based collaborative filtering, model-based collaborative filtering, content filtering, context filtering, and the hybrid method, were implemented to suggest relevant movies to customers. Each recommender system was experimented with three different TopN configurations, and the results were evaluated using information retrieval metrics.

Rice holds significant importance as a staple food and cultivated crop, ranking as the third most valuable food crop globally, following wheat and sorghum. This study focuses on leveraging data analytics and machine learning techniques to analyse rice-related data and establish correlations between fixed attributes to predict crop yield. The dataset pertains to Rice cultivation in Tamil Nadu over a span of 20 years and includes factors like area, production, yield, temperature, rainfall, humidity, and wind speed. The dataset underwent preprocessing to facilitate the application of data analytics and machine learning algorithms. The K-Means clustering algorithm was utilized to categorize rice productivity data, while the apriori algorithm was employed to extract association rules from the processed data. To predict yield, a spatial regression method was also utilized. Based on the data analysis results, employing a predefined k=3 clusters, the crop yield data from 28 districts were grouped into three clusters based on their proximity to the nearest centroid. Furthermore, it was observed that districts grouped together exhibited similar rice production levels.By applying the apriori method to the rice dataset, with a minimum support of 0.001 and a confidence level of 90%, numerous association rules was generated. Among these, 31 pertinent rules were identified for achieving "High Yield Production”. The study optimized the districts' rice crop yield using both spatial and non-spatial regression models, validating the results using metrics like R2 and Root Mean Square Error. The study's primary outcome is a collection of well-defined and effective association rules that can facilitate yield prediction. These findings are anticipated to be valuable for researchers, farmers, and government authorities aiming to enhance rice crop productivity

The notion of finding rare association rules is like finding precious gems in an open field; it is a daunting task but, if successful, it is very rewarding. Association rule mining systems, such as Apriori, generally employ an exhaustive search algorithm. While these algorithms are in theory capable of finding rare association rules, they become intractable if the minimum level of support is set low enough to find rare rules. Such algorithms are therefore inadequate for finding rare associations, and also suffer from the rare item problem. Research to solve this problem has become more prevalent in recent times. The main goal of rare association rule mining is to discover relationships among sets of items in a transactional database that occur infrequently. This chapter presents a survey on the current trends and approaches in the area of rare association rule mining.

Data warehouses and OLAP (online analytical processing) provide tools to explore and navigate through data cubes in order to extract interesting information under different perspectives and levels of granularity. Nevertheless, OLAP techniques do not allow the identification of relationships, groupings, or exceptions that could hold in a data cube. To that end, we propose to enrich OLAP techniques with data mining facilities to benefit from the capabilities they offer. In this chapter, we propose an online environment for mining association rules in data cubes. Our environment called OLEMAR (online environment for mining association rules), is designed to extract associations from multidimensional data. It allows the extraction of inter-dimensional association rules from data cubes according to a sum-based aggregate measure, a more general indicator than aggregate values provided by the traditional COUNT measure. In our approach, OLAP users are able to drive a mining process guided by a meta-rule, which meets their analysis objectives. In addition, the environment is based on a formalization, which exploits aggregate measures to revisit the definition of the support and the confidence of discovered rules. This formalization also helps evaluate the interestingness of association rules according to two additional quality measures: lift and loevinger. Furthermore, in order to focus on the discovered associations and validate them, we provide a visual representation based on the graphic semiology principles. Such a representation consists in a graphic encoding of frequent patterns and association rules in the same multidimensional space as the one associated with the mined data cube. We have developed our approach as a component in a general online analysis platform called Miningcubes according to an Apriori-like algorithm, which helps extract inter-dimensional association rules directly from materialized multidimensional structures of data. In order to illustrate the effectiveness and the efficiency of our proposal, we analyze a real-life case study about breast cancer data and conduct performance experimentation of the mining process.

Data mining in graph databases has received much attention. We have witnessed many algorithms proposed for mining frequent graphs. Inokuchi, Washio, and Nishimura (2002) and Karpis and Kumar (1998) introduce the Apriori-like algorithms, AGM and FSG, to mine the complete set of frequent graphs. However, both algorithms are not scalable as they require multiple scans of databases and tend to generate many candidates during the mining process. Subsequently, Yan and Han (2002) and Nijssen and Kok (2004) propose depth-first graph mining approaches called gSpan and Gaston, respectively. These approaches are essentially memory-based and their efficiencies decrease dramatically if the graph database is too large to fit into the main memory.

Chronic kidney disease (CKD) is a disorder in which the kidneys are weakened and become unable to filter blood. It lowers the human ability to remain healthy. The field of biosciences has progressed and produced vast volumes of knowledge from electronic health records. Heart disorders, anemia, bone diseases, elevated potassium, and calcium are the very prevalent complications that arise from kidney failure. Early identification of CKD can improve the quality of life greatly. To achieve this, various machine learning techniques have been introduced so far that use the data in electronic health record (EHR) to predict CKD. This chapter studies various machine learning algorithms like support vector machine, random forest, probabilistic neural network, Apriori, ZeroR, OneR, naive Bayes, J48, IBk (k-nearest neighbor), ensemble method, etc. and compares their accuracy. The study aims in finding the best-suited technique from different methods of machine learning for the early detection of CKD by which medical professionals can interpret model predictions easily.

In this paper, we describe a new framework for breaking symmetries in itemset mining problems. Symmetries are permutations between items that leave invariant the transaction database. Such kind of structural knowledge induces a partition of the search space into equivalent classes of symmetrical itemsets. Our proposed framework aims to reduce the search space of possible interesting itemsets by detecting and breaking symmetries between items. Firstly, we address symmetry discovery in transaction databases. Secondly, we propose two different approaches to break symmetries in a preprocessing step by rewriting the transaction database. This approach can be seen as an original extension of the symmetry breaking framework widely used in propositional satisfiability and constraint satisfaction problems. Finally, we show that Apriori-like algorithms can be enhanced by dynamic symmetry reasoning. Our experiments clearly show that several itemset mining instances taken from the available datasets contain such symmetries. We also provide experimental evidence that breaking such symmetries reduces the size of the output on some families of instances.