# Efficient Rule Generation for Associative Classification
## 1 Introduction  
Nowadays, there a number of classification techniques that have been applied to various real-world applications, i.e., graph convolutional networks for text classification [1], automated classification of epileptic electroencephalogram (EEG) signals [2], iris cognition [3], and anomaly detection [4]. Associative classification (AC) is a well-known classification technique that was first introduced by [PERSON] et al. [5]. It is a combination of two data-mining techniques, association rule mining, and classification. Association rule mining discovers the relationship between items in a dataset. Meanwhile, classification aims to predict the class label of any given instance from learning-labeled dataset. AC focuses on finding Class Association Rules (CARs) that satisfy certain minimum support and confidence thresholds in the form \(x\to c\), where \(x\) is a set of attribute values and \(c\) is a class label. AC has been reported in the literature to outperform other traditional classifiers [6; 7; 8; 9; 10; 11; 12; 13], In addition, a CAR is an if-then rule that can be easily understood by general users. Therefore, AC is applied in many fields, i.e., phishing website detection [6; 7; 11], heart disease prediction [8; 9], groundwater detection [12], and detection of low-quality information in social networks [10]. In traditional AC algorithms, minimum support threshold is a significant key parameter that is used to select frequent ruleitems and to then eliminate frequent ruleitems in which confidence values do not satisfy minimum confidence. This manner leads to a large number of frequent ruleitems. [PERSON] and [PERSON] [14] demonstrated that the number of 4 million frequent ruleitems can be generated when the minimum support threshold is set to 1%. Moreover, a number of AC-basedtechniques, i.e., Classification-based Association (CBA) [5], Fast Associative Classification Algorithm (FACA) [11], CAR-Miner-diff [14], Predictability-Based Class Collative Class Association Rules (PCAR) [15], Weighted Classification Based on Association Rules (WCBA) [16], and Fast Classification Based on Association Rules (FCBA) [17], create all possible CARs in order to determine a set of valid CARs that can be used in the classification process. Recently, Active Pruning Rules (APR) [13] has been proposed as a novel evaluation method. APR can be used to avoid generating all CARs. However, the exhaustive search for finding rules in classifiers may cause an issue in large datasets or low minimum support. Creating candidate CARs consumes intensive computational times and memory. The minimal process of candidate generation is still challenging because it is quite affected in terms of training time, input/output (I/O) overheads, and memory usage [18]. In this paper, a new algorithm is proposed to directly generate a small number of efficient CARs for classification. A vertical data format [19] is used to represent ruleitems associated with their transaction IDs. The intersection technique is used to easily calculate support and confidence values from the format. The ruleitems with 100% of confidence will be added to the classifier as a CAR. Whenever a CAR with 100% confidence is found, the transaction associated with the CAR will be removed by using a set difference to avoid generating redundant CARs. Finally, a compact classifier is built for classification. In conclusion, the contribution of this paper is as follows. 1. To avoid pruning and sorting processes, the proposed algorithm directly generates CARs with 100% confidence to build compact classifiers. The CARs with 100% confidence are anticipated to result in high prediction rates. 2. The proposed algorithm eliminates unnecessary transactions to avoid generating redundant CARs in each stage. 3. Simple set theories, intersection, and set difference are exploited to reduce computational time used in mining process and to reduce memory consumption. This paper is structured as follows. In Section 2, related works of AC are described.