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.
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