Upon the working principles of the human neocortex, the Hierarchical Temporal Memory model has been developed which is a proposed theoretical framework for sequence learning. Both categorical and numerical types of data are handled by HTM. Semantic Folding Theory (SFT) is based on HTM to represent a data stream for processing in the form of sparse distributed representation (SDR). For natural language perception and production, SFT delivers a solid structural background for semantic evidence description to the fundamentals of the semantic foundation during the phase of language learning. Anomalies are the patterns from data streams that do not follow the expected behavior. Any stream of data patterns could have a number of anomaly types. In a data stream, a single pattern or combination of closely related patterns that diverges and deviates from standard, normal, or expected is called a static (spatial) anomaly. A temporal anomaly is a set of unexpected changes between patterns. When a change first appears, this is recorded as an anomaly. If this change looks a number of times, then it is set to a “new normal” and terminated as an anomaly. An HTM system detects the anomaly, and due to continuous learning nature, it quickly learns when they become the new normal. A robust anomalous behavior detection framework using HTM-based SFT for improving decision-making (SDR-ABDF/P2) is a proposed framework or model in this research. The researcher claims that the proposed model would be able to learn the order of several variables continuously in temporal sequences by using an unsupervised learning rule.

Mining frequent patterns in transaction databases has been a popular theme in data mining study. Common activities include finding patterns among the large set of data items in database transactions. The Apriori algorithm is a widely accepted method of generating frequent patterns. The algorithm requires many scans of the database and thus seriously tax resources. Some of the methods currently being used for improving the efficiency of the Apriori algorithm are hash-based itemset counting, transaction reduction, partitioning, sampling, dynamic itemset counting etc. Two main approaches for associations rule mining are: candidate set generation and test, and restricted test only. Both approaches use to scan massive database multiple times. In our study, we propose a transaction patternbase, constructed in first scan of database. Transactions with same pattern are added to the Patternbase as their frequency is increased. Thus subsequent scanning requires only scanning this compact dataset which increases efficiency of the respective methods. We have implemented this technique with FP Growth method. This technique outperforms the database approach in many situations and performs exceptionally well when the repetition of transaction patterns is higher. It can be used with any associations rule mining method.