• Md. Irfan
• H. Ateeq Ahmed

In the current competitive business scenario, there is a need to analyze the competitive features and factors of an item that most affect its competitiveness.The evaluation of competitiveness always uses the customer opinions in terms of reviews, ratings and abundant source of information’s from the web and other sources. This paper develops an augmented competitor mining using product reviews. The itemsets are analyzed for selecting the relevant features. Using the c-miner, the frequent items are discovered and then represented by skyline operators. However, if all customer data is inserted into a database, the resulting records will provide a detailed profile of these customers and their interactions with one another, and will be an important resource for businesses that wish to probe customer data, customer needs, and customer satisfaction levels. Experimental analysis has shown the efficiency of the proposed algorithm

"Mining Large Unstructured Datasets to Find Top-K Competitors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 7, page no.662-664, July-2018, Available :http://www.jetir.org/papers/JETIR1807696.pdf

"Mining Large Unstructured Datasets to Find Top-K Competitors", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 7, page no. pp662-664, July-2018, Available at : http://www.jetir.org/papers/JETIR1807696.pdf