Interactive exploration technology is widely used to mine knowledge from various big data areas [ 3] and may be useful to rapidly and accurately detect phenotype-associated biomarkers from the huge amount of bio-OMIC data [ 4].
There are two work phases suggested namely data streaming [7] from twitter and mining knowledge through R-Studio.
Data mining technique is capable of mining valuable knowledge from large and changeable database.
Thus, this paper uses the Apriori algorithm as a methodology for association rules and clustering analysis for data mining, which is implemented for mining customer knowledge from the case firm, Phoenix Tours International, in Taiwan.
By collecting knowledge or information from conditions and the goal, we choose several knowledge as seed, then distribute it to all the department leaders, let them input relative knowledge including data mining knowledge to the platform.
The objective of the association rule mining is used to discover associations among items in a set, by mining essential knowledge from the database.
The presence of these expected patterns means that the Decision Tree algorithm, using the fragment descriptors, succeeded in mining relevant knowledge from the dataset.
When descriptions of data values are too detailed, the computational complexities involved in mining useful knowledge from the database generally increases.
As such, discovering or mining interesting knowledge from the BSN data stream is becoming an important issue to promote and assist important decision making in healthcare.
The proposed method is effective in mining valuable knowledge from available biomedical big data and achieves a good performance in bridging clinical data with genomic data for colorectal cancer.
