Future Trend Prediction of Indian IT Stock Market using Association Rule Mining of Transaction data

Abstract

The approach stated in this paper mainly focuses on minimizing the length of the transaction table of the stock market, based on some common features among the attributes which indirectly minimize the complexity involved in processing; we call this approach as Fragment Based Mining. This deals mainly with reducing the time and space complexity involved in processing the data. Experimentally we try to show our approach is promising one. We conclude that this approach can potentially be used for predictions and recommendations stock trading platforms.

Determination of the world stock indices' co-movements by association rule mining

“…Hoon Na and Sohn (2011) used the association analysis, and they stated that the Korean Stock Exchange index moved in the same direction with the US and European stock exchange indices. Argiddi and Apte (2012) used the association rule for the exchange of stocks in the India Information Technology index. In addition, it is tried to find solutions with two different methods in the study.…”

Application and performance of data mining techniques in stock market: A review

“…Price per annual earning, rumor or news to buy or sell, financial status of a company, book value of the trading year termed hybrid techniques. There are many instances of the application of single forecasts, such as artificial NNs (Nayak et al, 2014;Rodriguez et al, 2000;Thawornwong & Enke, 2004;), wavelet transformation (Gallegati, , 2008Hamrita & Trifi, 2011;O swięcimka et al, 2005), SVMs (Huang et al, 2005;Tay & Cao, 2002;Van Gestel et al, 2001), time series analysis (Devi et al, 2011), association rules (Argiddi & Apte, 2012;Lu et al, 1998;Ting et al, 2006;Umbarkar & Nandgaonkar, 2015), and decision trees (Adebimpe et al, 2012;Al-Radaideh et al, 2013;Saeedmanesh et al, 2010). Table 11 presents the single and hybrid techniques employed by researchers.…”

“…Such combinations are T A B L E 8 Duration of data . (2013),Egeli et al (2003),Kimoto et al (1990),Kuo et al (2001),Rodriguez et al (2000),Wunsch et al (1998),Hassan and Nath (2005),Naeini et al (2010),Schierholt and Dagli (1996),Roman and Jameel (1996),,Kumar et al (2013),Kazem et al (2013),Pai and Lin (2005),Chang and Liu (2008),Asadi et al (2012),Trippi and Desieno (1991),Versace et al (2004),Ayodele et al (2012),Diaconescu (2008),Vaisla and Bhatt (2010),Schumaker and Chen (2009),Argiddi and Apte (2012),Yang et al (2002),Tay and Cao (2002),Trafalis and Ince (2000),Huang et al (2008), Zhai et al (2007), Fung et al (2002), Wang et al (2005), Atsalakis and Valavanis (2009), Dai et al (2012), Alkhatib et al (2013), Hou et al (2013), Arasu et al (2014), Nayak et al (2014), Devi et al (2011), Al-Radaideh et al (2013), Hammad et al (2009), O swięcimka et al (2005), Cao and Tay (2001),Huarng and Yu (2012),Mittermayer (2004), Leu (1996(2009),Chen et al (2003),Mizuno et al (1998,Yao et al (1999),,Yao and Poh (1995),Choudhry and Garg (2008),Fan and Palaniswami (2001),Cruz et al (2003),Kumar and Thenmozhi (2006),Abraham et al (2003),Chen et al (2007),…”

On the mining of fuzzy association rule using multi-objective genetic algorithms

“…Association rule mining (Agrawal et al, 1993) originate, to find buying pattern of the customers through a process of market basket Analysis. Since then, association rule mining has been studied and applied in different areas like medical diagnosis (Rajendran and Madheswaran, 2010), stock market prediction (Argiddi and Apte, 2012), web mining (Chai and Li, 2010), network intrusion detection (Mao and Zhu, 2002), manufacturing (Wantanabe, 2010), recommender system (Xizheng, 2007), etc., and find patterns that associate different attributes (Tzacheva, 2012;Zhang et al, 2010).…”