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Sparseness is the distinctive aspect of big data generated by numerous applications at present. Furthermore, several similar records exist in real-world sparse datasets. Based on Iterative Trimmed Transaction Lattice (ITTL), the recently proposed TRICE algorithm learns frequent itemsets efficiently from sparse datasets. TRICE stores alike transactions once, and eliminates the infrequent part of each distinct transaction afterward. However, removing the infrequent part of two or more distinct transactions may result in similar trimmed transactions. TRICE repeatedly generates ITTLs of similar trimmed transactions that induce redundant computations and eventually, affects the runtime efficiency. This paper presents D-GENE, a technique that optimizes TRICE by introducing a deferred ITTL generation mechanism. D-GENE suspends the process of ITTL generation till the completion of transaction pruning phase. The deferral strategy enables D-GENE to generate ITTLs of similar trimmed transactions once. Experimental results show that by avoiding the redundant computations, D-GENE gets better runtime efficiency. D-GENE beats TRICE, FP-growth, and optimized versions of SaM and RElim algorithms comprehensively, especially when the difference between distinct transactions and distinct trimmed transactions is high.INDEX TERMS Big data applications, pattern recognition, association rules, frequent item set mining, IoT.

Sparseness is often witnessed in big data emanating from a variety of sources, including IoT, pervasive computing, and behavioral data. Frequent itemset mining is the first and foremost step of association rule mining, which is a distinguished unsupervised machine learning problem. However, techniques for frequent itemset mining are least explored for sparse real-world data, showing somewhat comparable performance. On the contrary, the methods are adequately validated for dense data and stand apart from each other in terms of performance. Hence, there arises an immense need for evaluating these techniques as well as proposing new ones for large sparse real-world datasets. In this study, a novel method: Mining Frequent Itemsets by Iterative TRimmed Transaction lattICE (TRICE) is proposed. TRICE iteratively generates combinations of varying-sized trimmed subsets of I , where I denote the set of distinct items in a database. Extensive experiments are conducted to assess TRICE against HARPP, FP-Growth, optimized SaM, and optimized RElim algorithms. The experimental results show that TRICE outperforms all these algorithms both in terms of running time and memory consumption. TRICE maintains a substantial performance gap for all sparse real-world datasets on all minimum support thresholds. Moreover, assessment of memory use of optimized SaM and RElim algorithms has been performed for the first time.INDEX TERMS Association rules, big data applications, data mining, frequent itemset mining, pattern recognition, pervasive computing.

Image modalities, such as computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission computed tomography (SPECT), and so on, reflect various levels of details about objects of interest that help medical practitioners to examine patients' diseases from different perspectives. A single medical image, at times, may not be sufficient for making a critical decision; therefore, providing detailed information from a different perspective may help in making a better decision. Image fusion techniques play a vital role in this regard by combining important details from different medical images into a single, information enhanced image. In this article, we present a novel weighted term multimodality anatomical medical image fusion method. The proposed method, as a first step, eliminates the distortions from the source images and afterward, extracts two pieces of crucial information: the local contrast and the salient structure. Both the local contrast and salient structure are later combined to obtain the final weight map. The obtained weights are then passed through a fast guided filter to remove the discontinuities and noise.Lastly, the refined weight map is fused with source images using pyramid decomposition to get the final fused image. The proposed method is accessed and compared both qualitatively and quantitatively with state-of-the-art techniques. The result illustrates the performance superiority and efficiency of the proposed method.

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