Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach

Monteiro, Rodrigo Bentes; Zimbrão, Geraldo; Schwarz, Holger; Mitschang, Bernhard; Souza, Jano Moreira de

doi:10.1007/11425274_31

Cited by 8 publications

(3 citation statements)

References 6 publications

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“…This system makes use of a router to adopt the previous mining result stored in the data warehouse, accordingly avoiding processing large amounts of the raw data. [8] Both fundamentalists and technicians have developed certain techniques to predict prices from financial news articles. In one model that tested the trading philosophies; LeBaron et.…”

Section: Related Workmentioning

confidence: 99%

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

Argiddi¹,

Apte²

2012

IJCA

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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.

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Section: Related Workmentioning

confidence: 99%

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

Argiddi¹,

Apte²

2012

IJCA

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“…This system makes use of a router to adopt the previous mining result stored in data warehouse accordingly avoiding processing large amounts of the raw data. The system of Data Warehouse of Frequent Item-sets (DWFIS) adopts different partitions to store the frequent item-sets (Monteiro et al, 2005).…”

Section: Related Workmentioning

confidence: 99%

Granule mining oriented data warehousing model for representations of multidimensional association rules

et al. 2008

IJIIDS

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To promise the quality of multidimensional association mining in real applications is a challenging research issue. The challenging issue is how to represent multidimensional association rules efficiently because of the complicated correlation between attributes. Multi-tier granule mining is one initiative in solving this challenge. This paper presents a granule mining oriented data warehousing model. It can divide attributes into tiers and discover granules for each tier from large multidimensional databases. In addition, it uses association mappings to generate association rules for describing the correlation between tiers. Experiments for the proposed model and the testing results are prosecuted.

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“…The maintenance of a DW of frequent itemsets has been already presented in [9]. Nonetheless, in our case data and patterns refer to trajectories instead of generic categorical data.…”

Section: Introductionmentioning

confidence: 99%

Frequent spatio-temporal patterns in trajectory data warehouses

Leonardi

Orlando

Raffaetà

et al. 2009

Proceedings of the 2009 ACM Symposium on Applied Computing

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Abstract. In this paper we present an approach for storing and aggregating spatio-temporal patterns by using a Trajectory Data Warehouse (TDW). In particular, our aim is to allow the analysts to quickly evaluate frequent patterns mined from trajectories of moving objects occurring in a specific spatial zone and during a given temporal interval. We resort to a TDW, based on a data cube model, having spatial and temporal dimensions, discretized according to a hierarchy of regular grids, and whose facts are sets of trajectories which intersect the spatiotemporal cells of the cube. The idea is to enrich such a TDW with a new measure: frequent patterns obtained from a data-mining process on trajectories. As a consequence these patterns can be analysed by the user at various levels of granularity by means of OLAP queries. The research issues discussed in this paper are (1) the extraction/mining of the patterns to be stored in each cell, which requires an adequate projection phase of trajectories before mining; (2) the spatio-temporal aggregation of patterns to answer roll-up queries, which poses many problems due to the holistic nature of the aggregation function.

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Building the Data Warehouse of Frequent Itemsets in the DWFIST Approach

Cited by 8 publications

References 6 publications

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

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

Granule mining oriented data warehousing model for representations of multidimensional association rules

Frequent spatio-temporal patterns in trajectory data warehouses

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