FPrep: Fuzzy clustering driven efficient automated pre-processing for fuzzy association rule mining

Abstract. Conventional Association Rule Mining (ARM) algorithms usually deal with datasets with binary values, and expect any numerical values to be converted to binary ones using sharp partitions, like Age = 25 to 60. In order to mitigate this constraint, Fuzzy logic is used to convert quantitative values of attributes to binary ones, so as to eliminate any loss of information arising due to sharp partitioning, especially at partition boundaries, and then generate fuzzy association rules. But, before any fuzzy ARM algorithm can be used, the original dataset (with crisp attributes) needs to be transformed into a form with fuzzy attributes. This paper describes a methodology, called FPrep, to do this pre-processing, which first involves using fuzzy clustering to generate fuzzy partitions, and then uses these partitions to get a fuzzy version (with fuzzy records) of the original dataset. Ultimately, the fuzzy data (fuzzy records) are represented in a standard manner such that they can be used as input to any kind of fuzzy ARM algorithm, irrespective of how it works and processes fuzzy data. We also show that FPrep is much faster than other such comparable transformation techniques, which in turn depend on non-fuzzy techniques, like hard clustering (CLARANS and CURE). Moreover, we illustrate the quality of the fuzzy partitions generated using FPrep, and the number of frequent itemsets generated by a fuzzy ARM algorithm when preceded by FPrep

Dimorphs of a Benzothiophene-quinoline Derivative with Distinct Mechanical, Optical, Photophysical and Conducting Properties

Because of distinct molecular conformations, packing modes, interaction types, and consequently their physicochemical properties, polymorphic forms of organic conjugated small molecules are intrinsically ideal for elucidating the relationship between their microstructures and the transcribed properties. Ethyl-2‐(1‐benzothiophene‐2‐yl)quinoline‐4‐carboxylate (BZQ) exists as dimorphs with distinct crystal habits―blocks (BZB) and needles (BZN). The crystal forms differ in their molecular arrangements―BZB has a slip-stacked column-like structure in contrast to a zig-zag crystal packing with limited π–overlap in BZN―and their photophysical and conducting properties. The BZB crystals characterized by extended π-stacking along [100] demonstrated semiconductor behavior, whereas the BZN, with its zig-zag crystal packing and limited stacking characteristics, was reckoned as an insulator. Monotropically related crystal forms also differ in their nanomechanical properties, with BZB crystals being considerably softer than BZN crystals. This discrepancy in mechanical behavior can be attributed to the distinct molecular arrangements adopted by each crystal form, resulting in unique mechanisms to relieve the strain generated during nanoindentation experiments. Waveguiding experiments on the acicular crystals of BZN revealed the passive waveguiding properties of the crystals. Excitation of these crystals using a 532 nm laser confirmed the propagation of elastically scattered photons (green) and the subsequent generation of inelastically scattered (orange) photons by the crystals. Further, the dimorphs display dissimilar photoluminescence properties; they are both blue-emissive, but BZN displays twice the quantum yield of BZB. This study underscores the integral role of polymorphism in modulating the mechanical, photophysical, and conducting properties of functional molecular materials. Importantly, our findings reveal the existence of light-emitting crystal polymorphs with varying electric conductivity, a relatively scarce phenomenon in the literature