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Data mining is a process of discovering patterns – usually relationships between variables, observations and events – by analyzing data. The term "data mining" was first used in the 1980s after the development of database systems required to manage "data warehouses" generated from relational databases. Data mining tools work by automating the discovery process, which involves using machine learning algorithms to uncover hidden trends and structures in large data sets.

Data Mining techniques can be categorized into two broad types: unsupervised and supervised.

In unsupervised methods, no target variable is specified so all the patterns are discovered purely based on correlations among input variables; otherwise known as exploratory analysis or prediscovery analysis . Commonly used approaches include clustering , association rule learning , regression/classification, density estimation and predictive data mining . Supervised methods require the target variable to be known or labeled. Common approaches include decision tree learning, artificial neural networks, logistic regression and support vector machines .

In addition to prediction of future values of target variables, data mining can also provide insight about past behavior. For example, an organization might wish to analyze its sales transactions in 2002 to develop a marketing plan for 2003. This includes analysis of whether customers purchased particular items together at the same time or within a short period of time after one another. It may discover that certain products are not selling as well as others and may adjust prices in order to improve profit margins or it may consider altering placement within stores or advertising campaigns in response to patterns observed.

Data used for data mining is often gathered from a variety of sources and processed to remove errors as part of the pre-processing, cleansing, integration, transformation or loading ( ETL ) phase before it can be analyzed by the data mining algorithms. Specialized software packages known as data mining tools have been developed which usually include tools for preprocessing, database connectivity and handling missing values among others in addition to providing features specifically aimed at improving effectiveness of pattern discovery and knowledge extraction. Data mining is advantageous because it allows researchers to formulate new hypotheses.

Information about certain events may help us understand why people shoplift as well as how retailers can reduce their losses from these thefts; information on employees' evaluations might provide employers with insight into their training programs or people's salaries could be used to predict how often they will change jobs; experience with a certain drug might help us understand its effectiveness and side effects.

Data mining techniques are also extensively employed in finance, marketing, fraud detection and prevention, counter-terrorism investigation , insurance, telecommunication and much more .

Important data mining concepts used in data mining homework assignments help

Some of the most important concepts associated with data mining assignment solutions provided by our experts are:

  • Data preprocessing: Basically involves cleaning up raw data so that it can be used by the subsequent prediscovery analysis and knowledge extraction phases. This is usually necessary because most real world datasets have missing values or contain erroneous entries which need to be corrected before any pattern discovery algorithms can work on them. Certain transformations such as normalization and scaling need to be applied in order to combat skewness and kurtosis which are common occurrences when dealing with real world datasets. It's also important to remove redundant, irrelevant or erroneous data from the raw dataset as this can result in efficiency problems during processing or even bias the outcome of any analysis by distorting the underlying relationships between variables present in the data .
  • Data integration: This is a process through which multiple similar datasets are merged into one single combined database. The new unified dataset must contain information on all columns that were present in each of the previously independent datasets but no additional information should be added beyond what was already included within those original files. In addition, mathematical transformations such as scaling may have to be performed in order to ensure compatibility between the different datasets prior to their combination.
  • Data cleaning: Is a process which involves resolving inconsistencies and errors within the combined dataset so that it can be used for subsequent pattern discovery analysis or knowledge extraction. The nature of these inconsistencies may vary dramatically depending on whether the data originated from an external source such as a survey, another company in the same industry, etc . , or was collected internally by the organization itself through various business processes such as customer order processing, employee performance evaluation etc . For example , raw data acquired externally may contain entries indicating that some customers purchased a product but left out information regarding what they bought while internally generated datasets might contain erroneous values resulting from incorrect human entry during some internal computerized transaction etc . The goal of data cleaning is to transform the combined dataset so that it contains accurate, objective information about different aspects of the business or other domain under study .
  • Data transformation: This involves applying a mathematical function on each data point in order to create new variables which better reflect a particular aspect of the underlying phenomenon being studied. For example , let's assume we want to use our sales data to predict customer likelihood of buying a certain product based on their previous purchases and salaries (both are considered as factors affecting purchasing decisions). A simple way to do this would be calculating mean salary for all customers who bought this item and then using it as an input variable during pattern discovery analysis along with previous purchase history. In contrast, transforming both salary and purchase history into z -scores prior to data mining analysis would result in a somewhat better outcome because it standardizes the values of these variables (reduces their impact on subsequent predictive models) and also allows us to assess whether customers who bought this item have significantly higher or lower average salary compared to all other customers.
  • If we perform transformation on any variable without taking into account its effect upon other relationships within the dataset, we may end up biasing our results towards some specific conclusions that do not necessarily reflect true underlying mechanisms at work . For example , transforming customer weight into z -score prior to data mining analysis will always make people with higher weights appear less attractive because z-scores map positive numbers onto the upper half of the axis and negative ones onto the lower half tends to make people with lower weights more attractive.
  • Data discretization: Is a process to group continuous variables into multiple categories in order to create new categorical variables that can be used as input during knowledge discovery analysis or classification . The continuous variable being converted into categories must meet certain criteria specified by the organization before this transformation is performed. For example , if our data consists of various soccer team's player statistics during each game they have played throughout the season, we may resort to discretization if we want to use these values in conjunction with different factors (such as position on field etc..) as input variables during pattern discovery analysis or classification. In other words, applying this technique helps us transform the continuous values representing each measure which captures some aspect of the underlying phenomenon into multiple categories which can later be used as either input variables or independent factors during knowledge discovery, predictive modeling or classification.

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Data mining assignment help is provided by highly qualified and experienced data science tutors. They have in-depth knowledge of the concepts behind this approach and can guide you through every step of the assignment . Our experts are always available to provide guidance on any data mining related problem encountered during class assignments or homework.

Additionally, our data mining assignment help will provide you with a comprehensive review that will ensure your success. This is important since failure to master all aspects of this subject might result in poor grades that will ultimately affect your GPA score unless you get help from us at So feel free to contact us if you need some clarification regarding any part of your Data Mining assignment or homework !

Our company offers data mining project help that will address each and every aspect of this subject in your class. If you need data mining assignment help, we are here to provide you with the most comprehensive guidance that will allow you to score the best grade possible.

Reliable data mining homework solution

Data mining is a process of deriving actionable knowledge from the various datasets stored in databases. The purpose of data mining is to extract information from enormous amount of raw data (often called as Big Data ) and convert it into useful, meaningful information that can be utilized for decision making. This approach has been gaining popularity among companies throughout the world because it allows them to gain insight into their customers' perceptions , preferences etc . at a much more detailed level than was previously possible without incurring any additional costs or time associated with gathering such information .

For example , let's assume we want to use our sales data to predict customer likelihood of buying a certain product based on their previous purchases and salaries (both are considered as factors affecting purchasing decisions). A very simplistic model might look like this:

We can see that dummy variables are used to convert non-numerical factors into real numbers. In practice, we would do it like this:

Use dummy variables for categorical factors

Categorical variables are usually represented as either 0 or 1 (or sometimes omitted completely) and their effect on other relationships within the dataset is negligible because either they have no effect at all (e.g . gender) or customers from one group behave in a very similar fashion to those from another group (e.g . customers whose ages fall into different ranges). However , when transforming categorical variables into numeric ones during data mining , one should always use dummy variable s instead of simply assigning the value 1 to every member of that category and 0 for everyone else . That's because a categorical variable is treated as an independent factor by statistical models if it is numeric (e.g . gender) but including dummy variables into such model will make it dependent on values from other factors included in the model ( e . g . income). Since some users might have income=0, which would lead us to conclude that there was somehow no connection between their income and the likelihood of buying our product when compared to other customers , which clearly isn't true based on our hypothesis.

Use multiple algorithms for data mining

The wide range of algorithms used in data mining , combined with the high level of subjectivity present during this process makes it very important to run several algorithms and use different parameter settings for each one of them while trying to achieve optimum results. In fact , this is a very common practice among professional data miners , which allows them to utilize all available information collected during the modeling process . So if you want to get an A on your data mining assignment, make sure you have at least 2 (if not more) predictive models ready ! However , be aware that implementing multiple algorithms on a single dataset can sometimes result in overfitting , especially if you're using decision trees .

Avoid overfitting in data mining projects

Overfitting denotes using too many input variables (as well as any other factors affecting statistical relationships within the model ) resulting in prediction error increasing rather than decreasing after training has finished. This happens because an overly complex/sophisticated model is unable to use the information it has acquired during the training process . This happens especially if one is using a single algorithm with many parameters and only small amount of data used for mining (a common mistake among students trying to complete their homework on time).

As you can see, a lot goes into modeling and even more when creating multiple models which are needed in practice. So make sure you spend enough time reading up on different aspects of data mining or by asking your professor or fellow students to help you with concepts that aren't as intuitive at first as they seem.

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Most of our members have BA and MA degrees in Finance, Mathematics, Statistics, Actuarial Science, Economics, Management or other subjects related to Business Administration. So if you need help with your boring data-analysis homework or want someone to create a winning tournament prediction model for you , visit our question form and find your data mining expert now!

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