Abstract-Problem of decision making, especially in financial issues is a crucial task in every business. Profit Pattern mining hit the target but this job is found very difficult when it is depends on the imprecise and vague environment, which is frequent in recent years. The concept of vague association rule is novel way to address this difficulty. Merely few researches have been carried out in association rule mining using vague set theory. The general approaches to association rule mining focus on inducting rule by using correlation among data and finding frequent occurring patterns. In the past years data mining technology follows traditional approach that offers only statistical analysis and discovers rules. The main technique uses support and confidence measures for generating rules. But since the data have become more complex today, it's a requisite to find solution that deals with such problems. There are certain constructive approaches that have already reform the ARM. In this paper, we apply concept of vague set theory and related properties for profit patterns and its application to the commercial management to deal with Business decision making problem.
Background: Osteoporotic compression fractures often progress to neurological impairment and severe pain, which results in restriction of mobility in elderly patients. Conventional open spinal decompression and stabilization in these patients have significant morbidities related to age, surgical approach, and blood loss. This case series evaluates the treatment of osteoporotic compression fractures at the thoracolumbar junction with short-segment stabilization with cement-augmented fenestrated pedicle screws and vertebroplasty using a minimally invasive percutaneous technique.Methods: Eleven patients aged 75 years or older who had osteoporotic vertebral fractures with worsening back pain and neurologic impairment were included in this study. Plain radiographs, magnetic resonance imaging, and computed tomography images of these patients were assessed. These patients underwent minimally invasive percutaneous stabilization with cementaugmented fenestrated pedicle screws and vertebroplasty with or without decompression. Preoperative and postoperative American Spinal Cord Injury Association score, visual analog scale (VAS) score, and Charlson Comorbidity Index were recorded. Cobb angle, spinal alignment, spinal canal encroachment, and anterior vertebral body height were recorded preoperatively and during each follow-up.Results: All patients neurologically recovered, and the VAS score significantly improved from an average of 9 before surgery to 2 immediately after surgery and 1 at final follow-up (P < 0.001). An average, local angle of kyphosis was 15° preoperatively, which decreased to 7° postoperatively (P < 0.01). The average anterior vertebral body height was 11 mm, which increased to 22 mm postoperatively (P < 0.001). No revision was required due to screw loosening or failure of construct. Conclusion:We concluded that patients with osteoporotic vertebral fractures treated with short-segment stabilization with cement-augmented fenestrated pedicle screws and vertebroplasty by minimally invasive percutaneous technique are associated with good clinical outcomes during an average follow-up of 18 months after spinal surgery.
Association rule mining (ARM) alone is a classical yet powerful method for basic rule discovery. However, generic measures being used are insufficient for specific pattern generation and rules of business interest. Critical decision making is a “key” component in contemporary businesses which could be rewarded by periodically utilizing patterns and rules to steer business growth and profit as well. To effectuate self-propelled growth in businesses, a feasible optimal recommender system needs to be accomplished without human intervention that recommends targeted product marketing and promotional strategies. In conjunction to ARM, uncertainty is a growing challenge in data mining research with facets of being probabilistic, fuzzy, or vague. Among many set theories to surmount uncertainty, vague set theory is employed for handling vagueness in data which gives the motivation of implementing a knowledge-based recommender framework by aggregating the two approaches to predict uncertain market growth strategy patterns and profitable rules.
Mining profit oriented patterns is a novel technique of association rule mining in data mining, which basically focuses on important issues related with business. As it is well known that every business aims to generate the profit and find the ways to improve the same. In earlier days association rule mining was used for market basket analysis and targeted only some of the business and commercial aspects. Afterwards the researchers started to aim the most prominent element of any business i.e. Profit, and determined the innovative way to generate the association rules based on profit. Profit oriented patterns mining approach combines the statistic based pattern mining with value-based decision making to generate those patterns with the maximum profit and some ways to generate recommenders for future strategy. To achieve the desired goal the traditional association rule mining alone is not effectual, so we combine the strength of genetic algorithm with association rule mining to enhance its capability. The study shows that Genetic Algorithm improves the effectiveness and efficiency of association rule mining outcome, since genetic algorithms are competent to handle the problems related with the uncertainty, multi-dimensional, non-differential, non-continuous, and non-parametrical, non-linearity constraint and multi-objective optimization problems. In this paper we apply the concept of profit pattern mining with genetic algorithm to generate profit oriented pattern which help out in future business expansion and fulfill the business objective.
Problem of decision making is a crucial task in every business. Profit Pattern Mining hit the target by minimizes the gap between statistical based pattern generation and value base decision making. But this job is found very difficult when it depends on the large, imprecise and vague environment, which is frequent in recent years. The concept of soft computing with data mining is novel way to address this difficulty. The general approaches to association rule mining focus on inducting rule by using correlation among data and finding frequent occurring patterns. The major technique uses support and confidence measures for generating rules which is not adequate nowadays as a measure of interest, since the data have become more multifaceted these days, it's a necessary to find solution that deals with such problems and uses some new measures like profit, significance etc. In this chapter, authors apply concept of pattern mining with vague set theory, Genetic algorithm theory and related properties to the commercial management to deal with business decision making problem.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.