  A wide range of Sensor Networks (SNs) are deployed in real world applications
which generate large amount of raw sensory data. Data mining technique to
extract useful knowledge from these applications is an emerging research area
due to its crucial importance but still its a challenge to discover knowledge
efficiently from the sensor network data. In this paper we proposed a
Distributed Data Extraction (DDE) method to extract data from sensor networks
by applying rules based clustering and association rule mining techniques. A
significant amount of sensor readings sent from the sensors to the data
processing point(s) may be lost or corrupted. DDE is also estimating these
missing values from available sensor reading instead of requesting the sensor
node to resend lost reading. DDE also apply data reduction which is able to
reduce the data size while transmitting to sink. Results show our proposed
approach exhibits the maximum data accuracy and efficient data extraction in
term of the entire networks energy consumption.
