Search for Window Treatments-1

Yesterday I and my family visited one mall. It is very big and well known shopping mall in our area. I was there to buy something unique and different for my house. I saw so many antique things which are useful to decorate the house like wall picture, flower vas etc…. Actually I was interested in window treatments items. Fortunately I found one store and they have many types of blinds on their display. For the first time I have seen these much collections of blinds at one place. The way they have displayed their product range was so attractive.

They are having window blind & shade products. That includes wood blinds, faux wood blinds, mini blinds, and vertical blinds. Their window shades includes roman shades, cellular shades, pleated shades, roller shades, and woven wood bamboo shades. I was interested in vertical blinds for my drawing room. Salesman their explain me vertical blinds are very popular and appealing for sliding glass doors and larger windows. He told me to select between cord and chain operation and wand controlled blinds. My kids are already grown so, I don’t have any threat to my kids safety. We decided to go with chain instead of cord.

Than he told us to select from a variety of wood, fabric patterns, and basic vinyl on our verticals so that our blinds are coordinated with the rest of our decorations. He told us that for more unique look, sliding panel shades offer both shade and safety with their baton draw design. These large panels will add ambiance to any formal room in your home! So, at last I decided to go with vertical blinds for my drawing room.

Data Mining -2

Forecasting, or predictive modeling provides predictions of future events and may be transparent and readable in some approaches (e.g., rule-based systems) and opaque in others such as neural networks. Moreover, some data-mining systems such as neural networks are inherently geared towards prediction and pattern recognition, rather than knowledge discovery.

Metadata, or data about a given data set, are often expressed in a condensed data-minable format, or one that facilitates the practice of data mining. Common examples include executive summaries and scientific abstracts.

Data mining relies on the use of real world data. This data is extremely vulnerable to collinearity precisely because data from the real world may have unknown interrelations. An unavoidable weakness of data mining is that the critical data that may expose any relationship might have never been observed. Alternative approaches using an experiment-based approach such as Choice Modelling for human-generated data may be used. Inherent correlations are either controlled for or removed altogether through the construction of an experimental design.

Recently, there were some efforts to define a standard for data mining, for example the CRISP-DM standard for analysis processes or the Java Data-Mining Standard. Independent of these standardization efforts, freely available open-source software systems like RapidMiner and Weka have become an informal standard for defining data-mining processes.

Data Mining -1

We are talking data mining in our last post and we were talking background of data mining. We continue our talk on data mining in this post.


Data mining identifies trends within data that go beyond simple analysis. Through the use of sophisticated algorithms, non-statistician users have the opportunity to identify key attributes of business processes and target opportunities. However, abdicating control of this process from the statistician to the machine may result in false-positives or no useful results at all.


Although data mining is a relatively new term, the technology is not. For many years, businesses have used powerful computers to sift through volumes of data such as supermarket scanner data to produce market research reports (although reporting is not always considered to be data mining). Continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy and usefulness of data analysis.


The term data mining is often used to apply to the two separate processes of knowledge discovery and prediction. Knowledge discovery provides explicit information that has a readable form and can be understood by a user.