Data mining is the most common research area in the field of computer science and allied areas. Decision making in clinical data mining plays a significant role in patient’s life. In this survey research article we aim to portray various data mining algorithms, soft computing techniques, machine learning algorithms and bio-inspired algorithms for predicting / classifying heart disease. Several ...

In current market scenarios, telecom companies are quite competitive and look forward to have lion’s share in the market by winning new and withholding existing customers. Customers who are lost to competitor are known as Churned customers and can be retain by adopting Churn prevention model. For a given dataset, this model predicts the list of customers to be churned in future enabling the res...

Data mining is a process where intelligent methods are applied in order to extract data patterns. This is used in cases of discovering patterns and trends among large datasets. Data classification involves categorization of data into different category according to protocols. They are many classification algorithms available and among the decision tree is the most commonly used method. Classifi...

one of the most important issues related to knowledge discovery is the field of comment mining. opinion mining is a tool through which the opinions of people who comment about a specific issue can be evaluated in order to achieve some interesting results. this is a subset of data mining. opinion mining can be improved using the data mining algorithms. one of the important parts of opinion minin...

This study clusters customers and finds the characteristics of different groups in a life insurance company in order to find a way for prediction of customer behavior based on payment. The approach is to use clustering and association rules based on CRISP-DM methodology in data mining. The researcher could classify customers of each policy in three different clusters, using association rules. A...

This paper highlights the significance of classification in data mining and knowledge discovery. In this paper we investigate the performance of various data mining classification algorithms viz. Rnd Tree, Quinlan decision tree algorithm (C4.5), KNearest Neighbor algorithm etc., on a large dataset from the „Wisconsin Breast tissue dataset‟ (derived from the UCI Machine Learning Repository) that...

The returns policy has long been considered as a critical yet controversial issue in the development of supply chain and marketing strategies. Up-stream manufacturers or distributors may offer returns policies to the down-stream retailers or customers to increase order and sales quantities. There are trade-offs between returns policies and customer satisfaction, product sales, and operating cos...

Frequent item sets mining from the transaction dataset is one of the most challenging problems in data mining approaches. In many real world scenarios, the information is not extracted from a single data source, but from distributed and heterogeneous ones. Therefore, the discovered knowledge in this paper is generating association rules using frequent pattern growth algorithms for transactional...

Since the PoC, Intel sales and marketing teams have delivered communications to eight vertical industries across four geographies and in eight languages. When we tracked resellers in the engagement chain, we found that, in comparison with the rest of the sales pipeline, twice as many resellers advanced from leads to qualified leads. Their click-through rate for email newsletters is now three ti...