Data Mining for Evolving Fuzzy Association Rules for Predicting Monsoon Rainfall of India

Dhanya, C. T.; Kumar, D. Nagesh

doi:10.1515/jisys.2009.18.3.193

Cited by 15 publications

(2 citation statements)

References 8 publications

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“…This helps to represent the semantic content of the sensor data more efficiently than the binary association rule by providing meaningful linguistic labels of sensor data (Chen and Chen 2007). Moreover, using fuzzy membership functions of fuzzy sets overestimation or underestimation of the boundary values of binary association rule can be addressed by allowing partial membership to different fuzzy sets (Dhanya and Kumar 2009).…”

Section: Fuzzy Association Rulementioning

confidence: 99%

A novel anomaly detection algorithm for sensor data under uncertainty

2016

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It is an era of Internet of Things, where various types of sensors, especially wireless, are widely used to collect huge amount of data to feed various systems such as surveillance, environmental monitoring, and disaster management. In these systems, wireless sensors are deployed to make decisions or to predict an event in a real-time basis. However, the accuracy of such decisions or predictions depends upon the reliability of the sensor data. Unfortunately, erroneous data are received from the sensors. Consequently, it hampers the appropriate operations of the mentioned systems, especially in making decisions and prediction. Therefore, the detection of anomaly that exists with the sensor data drew significant attention and hence, it needs to be filtered before feeding a system to increase its reliability in making decisions or prediction. There exists various sensor anomaly detection algorithms, but few of them are able to address the uncertain phenomenon, associated with the sensor data. If these uncertain phenomena cannot be addressed by the algorithms, the filtered data into the system will not be able to increase the reliability of the decision-making process. These uncertainties may be due to the incompleteness, ignorance, vagueness, Communicated by imprecision and ambiguity. Therefore, in this paper we propose a new belief-rule-based association rule (BRBAR) with the ability to handle the various types of uncertainties as mentioned.The reliability of this novel algorithm has been compared with other existing anomaly detection algorithms such as Gaussian, binary association rule and fuzzy association rule by using sensor data from various domains such as rainfall, temperature and cancer cell data. Receiver operating characteristic curves are used for comparing the performance of our proposed BRBAR with the aforementioned algorithms. The comparisons demonstrate that BRBAR is more accurate and reliable in detecting anomalies from sensor data under uncertainty. Hence, the use of such algorithm to feed the decision-making systems could be beneficial. Therefore, we have used this algorithm to feed appropriate sensor data to our recently developed belief-rule-based expert system to predict flooding in an area. Consequently, the reliability and the accuracy of the flood prediction system increase significantly. Such novel algorithm (BRBAR) can be used in other areas of applications.

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Section: Fuzzy Association Rulementioning

confidence: 99%

A novel anomaly detection algorithm for sensor data under uncertainty

2016

View full text Add to dashboard Cite

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“…Kemampuan yang paling penting adalah self-organizing data mining yang dapat dibuktikan melalui enhanced Group Method of Data Handling (e-GMDH) yang sudah diimplementasikan untuk memperkirakan variabel cuaca seperti suhu, jumlah/tinggi curah hujan dalam satu bulan, dan tekanan udara harian [7]. Data mining dengan teknik lain yaitu Fuzzy Association Rule juga telah dipergunakan untuk memperkirakan curah hujan Moonson di kawasan India [8]. Beberapa penelitian memanfaatkan metode-metode yang ada dalam data mining secara bersamaan.…”

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