Exploiting Data Mining Techniques for Improving the Efficiency of Time Series Data Using Spss-Clementine
Pushpalata Pujari, Jyoti Bala Gupta · Researchers World – Journal of Arts Science & Commerce · 2012
ABSTRACTThe research work in data mining has achieved a high attraction due to the importance of its applications This paper addresses some theoretical and practical aspects on Exploiting Data Mining Techniques for Improving the Efficiency of Time Series Data using SPSS-CLEMENTINE. This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs. In this paper, we propose utilizes the famous data mining software SPSS Clementine to mine the factors that affect information from various vantage points and analyze that information. However the purpose of this paper is to review the selected software for data mining for improving efficiency of time series data. Data mining techniques is the exploration and analysis of data in order to discover useful information from huge databases. So it is used to analyze a large audit data efficiently for Improving the Efficiency of Time Series Data. SPSS- Clementine is object-oriented, extended module interface, which allows users to add their own algorithms and utilities to Clementine's visual programming environment. The overall objective of this research is to develop high performance data mining algorithms and tools that will provide support required to analyze the massive data sets generated by various processes that is used for predicting time series data using SPSS- Clementine. The aim of this paper is to determine the feasibility and effectiveness of data mining techniques in time series data and produce solutions for this purpose.Keywords: Time series data; Data mining; Forecasting; Classification; SPSS-Clementine.INTRODUCTION:Classification algorithm has discrete allowing predicting the relationship between input data sets. Commercial data mining software's are considerably expensive to purchase and the cost of training involved is high. For this, the best software that fits business needs is very important, crucial and difficult to decide the forecasting of any type of data set. As well as modern data analysis has to cope with tremendous amounts of data. The modern economy has become more and more information-based [5]. The widespread uses of information technology, a large number of data are collected which results in massive amounts of data. Such time-ordered data typically can be aggregated with an appropriate time interval, yielding a large volume of equally spaced time series data [6]. Such data can be explored and analyzed using many useful tools and methodologies developed in modern time series analysis. Data mining is the exploration and analysis of data in order to discover meaningful patterns [7]. Data mining techniques have been used to uncover hidden patterns and predict future trends. The competitive advantages achieved by data mining include increased revenue, reduced cost, and much improved marketplace responsiveness and awareness. The term data mining refers to information elicitation [8]. It is an interdisciplinary field that combines artificial intelligence, computer science, machine learning, data-base management, data visualization, mathematics algorithms and statistics [9]. This technology provides different methodologies for decision-making, problem solving, analysis, planning, diagnosis, detection, integration, prevention, learning and innovation.The approach presented in this paper is a general one and can be applied to any time series data sequence. An improvement of technological process control level can be achieved by time series analysis in order to prediction of their future behavior using SPSS- Clementine. SPSS Clementine is very suitable as a mining engine with its interface and manipulating modules that allow data exploration, manipulation and exploration of any interesting knowledge patterns [1]. The paper deals with the utilization of data mining using SPSSClementine to fix best the prediction of time series. We can find an application of this prediction by the control in production of energy, heat, and etc. …