Data Mining Thailand

 The Info Factory are the specialists of Data mining in Thailand. The premier company with extensive experience of extracting useful, revealing and practical information for you and your business. Our business intelligence development team is able process your companies’ data sets and form well-designed, analytical conclusions that will benefit all parts of your business in Thailand.

 Our statistical and computer programming knowledge, combined with our extensive experience of Thailand’s people and culture means we are the premier company to help drive your company forward utilizing data mining in Thailand.

 By combining mathematical algorithms and equations with statistical techniques; we can extract trends and patterns that can be applied to advertising and marketing effectiveness; e-commerce initiatives, supply chain processes and many other elements of business. By extracting actionable information we can help you push your company forward by fine tuning processes, increasing productivity and increasing efficiency. Data mining will help sharpen your marketing drives, slicken your supply chain processes and increase efficiency across the board.

 We are in the information economy and its only getting bigger as data continues to grow consider social media: linked in, Facebook, twitter- this is fundamentally all more data that describes people: what they do, what they like, who they are- the way to extract this abundance of information is with data mining. By not using all the information at your disposal your company will always fall short of your potential especially when it comes to meeting the need of your customers. Data Mining in Thailand gives you the opportunity to be ahead of the curve in an ever expanding market.

Pre-processing

 Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data-set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. We can glean our own information from various Data Marts and Warehouses, or you can provide your own data sets if you have a very specific goal you are looking to achieve. The target set is then cleaned; data cleaning removes outlying irregularities, observations containing noise and those with missing data

Results validation

 Once we have extracted information via the data set, we then validate our results. Not all patterns found by the data mining algorithms are necessarily valid. Due to overfitting from complex algorithms, we test samples again to make sure the conclusions reached are accurate.

Data mining

Data mining involves six common classes of tasks

  •    Deviation detection – The identification of anomalies in data records that might be interesting or data errors and require further investigation.
  •    Clustering –is the task of discovering groups and structures in the data that are in some way or another “similar”, without using known structures in the data.
  •    Dependency Modeling – Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using dependency modeling, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  •    Classification– is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as “legitimate” or as “spam”.
  •    Regression – Attempts to find a function which models the data with the least error.



data mining