Online social networks (OSNs) provide services targeting multifarious types of users in order to attract and retain them. For this purpose, developing new services according to user preferences has recently been under focused by various researchers. Most of present studies focus only on extracting the behavioral patterns of users, and neglect users’ interactions, which is the main part of the social activities in OSNs. To cope with this issue, this paper proposes a new methodology to bring both dimensions of data, the extracted behavioral patterns of users and their social interactions, in order to reach a better analysis. Moreover, the idea provides a basis for considering other dimensions efficiently. In order to evaluate the performance of the methodology, this paper performs a case study, and conducts a set of experiments on the computer-generated datasets. The results indicates the great performance of the methodology.
Nowadays, many organizations focus on discovering their customers’ hidden patterns to maintain their competitive position through customer analysis. In fact, more and more organizations are realizing that customers are their most valuable resources. This paper performs a research using data associated with 300 clients of an insurance company in city of Anzali, Iran and they are analyzed using K-Means clustering method. Using demographic variables including gender, age, occupation, education level, marital status, place of residence and clients’ incomes, the study determines the optimal numbers of clusters in order to achieve necessary data for grouping customers. Next, the study uses the method of association rules to find hidden patterns for the insurance industry.
Data mining is the technique to find hidden patterns from a very large volume of historical data. Association rule is a type of data mining that correlates one set of items or events with another set of items or events. Another data mining strategy is clustering technique. This technique is used to create partitions so that all members of each set are similar according to a specified set of metrics. Both the association rule mining and clustering helps in more effective individual and group decision making for optimal inventory control. Owing to the above facts, association rules are mined from each cluster to find frequent items and then loss profit is calculated for each frequent item. Initially, the clustering algorithm is used to partition the transactional database into different clusters. Apriori, a classic data mining algorithm is utilized for mining association rules from each cluster to find frequent items. Later the loss profit is calculated for each frequent item. The obtained loss profit is used to rank frequent items in each cluster. Thus, the ranking of frequent items in each cluster using the proposed approach greatly facilitate optimal inventory control. An example is illustrated to validate the results.
Timely identification of newly emerging trends is needed in business process. Data mining techniques like clustering, association rule mining, classification, etc. are very important for business support and decision making. This paper presents a method for redesigning the ordering policy by including cross-selling effect. Initially, association rules are mined on the transactional database and EOQ is estimated with revenue earned. Then, transactions are clustered to obtain homogeneous clusters and association rules are mined in each cluster to estimate EOQ with revenue earned for each cluster. Further, this paper compares ordering policy for imperfect quality items which is developed by applying rules derived from apriori algorithm viz. a) without clustering the transactions, and b) after clustering the transactions. A numerical example is illustrated to validate the results.
With ever-increasing demands for high surface finish and complex shape geometries on various difficult-to-machine materials, conventional metal removal methods are now being replaced by non-traditional machining (NTM) processes. These NTM processes use energy in its direct form to remove material from the workpiece surface. They are also cost effective for a wide range of micro- and nano-level applications. For effective utilization of different NTM processes, it is quite important to study their characteristics and material removal mechanisms in order to identify the most significant control parameters affecting the process responses. In this paper, a data mining approach using classification and regression tree algorithm is employed to identify the most important input parameters of three NTM processes, i.e. micro electro discharge milling process, wire electrical discharge machining process and laser beam machining process. The derived observations are also validated using the analysis of variance results.
Case-based reasoning (CBR) is the process of solving new cases by retrieving the most relevant ones from an existing knowledge-base. Since, irrelevant or redundant features not only remarkably increase memory requirements but also the time complexity of the case retrieval, reducing the number of dimensions is an issue worth considering. This paper uses rough set theory (RST) in order to reduce the number of dimensions in a CBR classifier with the aim of increasing accuracy and efficiency. CBR exploits a distance based co-occurrence of categorical data to measure similarity of cases. This distance is based on the proportional distribution of different categorical values of features. The weight used for a feature is the average of co-occurrence values of the features. The combination of RST and CBR has been applied to real categorical datasets of Wisconsin Breast Cancer, Lymphography, and Primary cancer. The 5-fold cross validation method is used to evaluate the performance of the proposed approach. The results show that this combined approach lowers computational costs and improves performance metrics including accuracy and interpretability compared to other approaches developed in the literature.
Nowadays credit scoring is an important issue for financial and monetary organizations that has substantial impact on reduction of customer attraction risks. Identification of high risk customer can reduce finished cost. An accurate classification of customer and low type 1 and type 2 errors have been investigated in many studies. The primary objective of this paper is to develop a new method, which chooses the best neural network architecture based on one column hidden layer MLP, multiple columns hidden layers MLP, RBFN and decision trees and ensembling them with voting methods. The proposed method of this paper is run on an Australian credit data and a private bank in Iran called Export Development Bank of Iran and the results are used for making solution in low customer attraction risks.