In recent years, data has become increasingly larger not only in rows (i.e. number of instances) but also in columns (i.e. number of features). in many applications, such as gene selection from micro-array data and text automatic categorization, the number of features in the raw data ranges from hundreds to tens of thousands. High dimensionality brings great difficulty in pattern recognition, machine learning and data mining. As data reduction is one of the well-known technics in data preprocessing. With the development of science and the apparent lack of knowledge of the universe and the ability to interpret the phenomena of the universe by using the implications of acquisition These concepts can be used to solving problems in computer science and make a major contribution in this field. One of the common areas, is using of the quantum mechanics concepts in order to develop efficient algorithms. Due to the importance of data reduction, With the concepts study quantum mechanics and atomic models , the proposed quantum model was used to solve and modeling the feature selection problem. In this model, features such as electrons are around the nucleus of an atom and are distributed around it, and as the electrons move around the nucleus of an atom. The best layer for Features around the nucleus of an atom is obtained during the execution of the algorithm and the core features are ignored. Supervised clustering using the proposed algorithm will be able to simultaneously create centers of clusters in order to kashida; TEXT-ALIGN: justify; LINE-HEIGHT: 17pt; TEXT-KASHIDA: 0%; MARGIN: 0cm 0cm 10pt" Keywords: Data reduction, Quantum feature selection, Quantum atomic model, Feature weighting, Quantum clustering
