In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...

The aim of this study was to propose and validate the new scaling method, fuzzy partial credit scaling (FPCS), which combines fuzzy set theory with the partial credit model (PCM) to score rating scales. To achieve this goal, the Chinese version of BDI (Beck Depression Inventory-II) was administrated to a depressed sample of patients and a non-depressed sample. The depressed sample consisted of ...

There are many clustering algorithms for gene expression data in the literature that are robust against noise and outliers. The limitation with many of these algorithms is that they cannot identify the overlapping and intersecting clusters. This paper presents an algorithm for clustering gene expression data using the concepts of common neighbors and fuzzy clustering for detecting intersecting ...

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

This paper presents two clustering methods: the first one uses a density-based approach (DGC) and the second one uses a frequent itemset mining approach (FINN). DGC uses regulation information as well as order preserving ranking for identifying relevant clusters in gene expression data. FINN exploits the frequent itemsets and uses a nearest neighbour approach for clustering gene sets. Both the ...

In this paper, a cluster validity measure is presented to infer the appropriateness of data partitions. The proposed validity measure adopts a novel non-metric distance measure based on the idea of "point symmetry". The proposed validity measure can be applied in finding the number of clusters of different geometrical structures. The performance evaluation of the validity measure compares favor...

this paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in credit scoring. yet there isn’t any literature in the field of data mining applications in credit scoring. using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the science direct onli...

The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extract...

This paper addresses the question of what determines a poor credit score. We compare estimated credit scores with measures of impulsivity, time preference, risk attitude, and trustworthiness, in an effort to determine the preferences that underlie credit behavior. Data is collected using an incentivized decision-making lab experiment, together with financial and psychological surveys. Credit sc...

A heuristic approach to possibilistic clustering is the effective tool for the data analysis. The approach is based on the concept of allotment among fuzzy clusters. To establish the number of clusters in a data set, a validity measure is proposed in this paper. An illustrative example of application of the proposed validity measure to the Anderson’s Iris data is given. A comparison of the vali...