Data Mining and the creation of a proprietary algorithmic methodology, strategy and technologies.

Please see "Advanced Description" for full project detials.

## Deliverables

I and a few business, technological and engineering experts are in the process of creating a new venture. We are creating an application developed and target for Google Android mobile devices, including smartphones and tablets. This is pretty much all that we can reveal publicly without a non disclosure, non compete and confidentiality contract in place. We are in the final stages of becoming well funded, and each participant contributes in their respective field of expertise.

We are currently seeking additional well qulifed experts as partners or part time employees to further contribute to our goal of bringing our concept into reality.

If you are an expert in the following areas, please private message me to further discuss the opportunity and possibility of joining our venture.

1. **_Data mining:_**_._

1. Having a complete understanding of Knowledge Discovery in Databases (KDD) process with the following defined with the stages:

(1) Selection

(2) Pre-processing

(3) Transformation

(4) Data Mining

(5) Interpretation/Evaluation.

In addition to (KDD) you would need to have a thorough understanding of Cross Industry Standard Process for Data Mining (CRISP-DM), which is defined below in six phases:

(1) Business Understanding

(2) Data Understanding

(3) Data Preparation

(4) Modeling

(5) Evaluation

(6) Deployment

Data mining involves six common classes of tasks, each of which you are expected to have a complete and thorough understanding of points 1 threw 6 with emphasis on points number 2, 3, 4 and 5.

1. Anomaly detection (Outlier/change/deviation detection) - The identification of unusual data records, that might be interesting or data errors and require further investigation.

1. Association rule learning (Dependency modeling) - Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, 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.

3. 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.

4. 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".

5. Regression - Attempts to find a function which models the data with the least error.

6. Summarization - providing a more compact representation of the data set, including visualization and report generation.

**2.** **_Mathematics and Statistics:_**

**3.** **_The creation of complex algorithms and methodologies using data collected from data mining:_**

Among other responsibilities, your main responsibility will be to enable our engineers and programmers to build a proprietary multilateral algorithmic methodology and strategy using data mining that you developed for our project. Besides the above requirements, it would be preferred if not essential to have a good understanding of technology and business.

We are ideally looking for interested parties that would consider working on an equity/partnership basis.

Habilidades: Análisis de negocios, Planes de negocios, Estadísticas

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Información del empleador:
( 32 comentarios ) United States

Nº del proyecto: #2779112

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