Data about design, cost/benefit trades, issues and project status can be grasp more quickly when the data is presented using information visualization tools.
People more quickly understand the implications of data that is summarized in graphic form. Whether a manager needs additional resources or is trying to persuade a potential client of the benefits of his project or product, he will often do so more effectively with data.
When a manager needs to present complex relational data, recommendations supported by detailed cost/benefit analysis, or project status at a glance, information visualization tools are a great help. The audience’s understanding precedes their acceptance. Continue reading “Managers Need Skills in Data Visualization”
Leaders can focus on a few ways to reduce stress at work, becoming the example and helping to provide balance and increase productivity.
Balancing time, work and all the priorities that come with life is never easy. Worst of all is that lack of balance leads to stress. In order to reduce stress and find balance, there are specific steps a leader can take to ensure that they exemplify balance and assist their team in achieving their own. Continue reading “Reduce Stress at Work”
To find out how fast a firm is growing or is expected to grow, analysts must examine a set of ratios that indicate what the firm’s sustainable growth potential is.
A company’s growth potential analysis is of paramount importance to both lenders and investors. Investors want to be assured that their investments will generate at least the required rates of return, if not exceed them, for they recognize that the future growth rate of a company is positively correlated to growth rates of its earnings and cash flows.
Lenders are also very interested in a company’s analysis of growth potential because they need to know whether the company will be able to meet its financial obligations or not.
Notably, the more a company is growing, the more money is left over and available to cover any outstanding liabilities due to the lender.
For example, some lenders watch for ratios that use book values of a company’s assets. The assumption is that selling at book values is the worst case scenario.
Consequently, if there is enough money left over to cover what is owed to the lender after selling off the company’s assets at book values, the lender is generally considered to be in good shape. Continue reading “Analyzing Growth Potential”
Business information is critical for strategy formulation, marketing plan development, economic sourcing of materials and supplies and other business needs.
Along with scientific, technology & medical and education/training information, business information delivery has been transformed by the Web.
Business information consists mainly of market research reports for different industries/regions, company information including credit and financial info, economic analyses of countries and industries and news on developments of business interest.
How is Business Information Used?
As discussed in the article on business strategy development, strategy formulation depends heavily on gathering information about external environment, market trends, competitors, technology developments, and prospective customers and their plans.
How Organization Structure and Policy can Create Success
Imagine a workplace where everyone is allowed to do whatever they want.
It is not as risky as it first sounds looking at the new context of information access and knowledge. A task for management is to set forth what has to be done first in order to allow people to do what they want without creating total chaos. This is an empowerment that does not equal anarchy.
The growing business today has focused deeply on Big Datause, this is to drive insights in business and unveil potential profits in it. As what other magazines and books have been telling, Big Data simply offers a precision and scale in your operation. This handy feature enables firms to determine the capacity of their assets.
We will use “asset” as our term, since this one communicate to something that has value and something that has the potential in which it can be traded or be managed in different form.
When we talk “asset” it also reminds us on the underpinning economic activities and by using these data in order to earn profits. In markets, assets are being formed in all types. A simple data can even provide a comprehensive asset view.
The surveillance in asset is a precious and the managing of assets is economically understandable. With this, firms are willing to acquire this information. The truth now entrenched deeply on how a market operate and survive after experiencing data wave.
Technology is now enabling data to be analyzed and creates another data in which assets are measured. This includes those assets that are difficult to measure during the old days.
The changes in capturing data from a passive process only figure out two things. More data is captured, since there is no human interaction in it.
Second is the data capture omission that is more but less controlled, this means that data is needed to understand possible risk and even anomalies will be collected without any interference.
In this long article, I am going to share and somewhat explain to you about six of the best algorithms developed in the data mining world.
And of course the data that I am going to share in this content is back-up by different panels.
After knowing the list that I am going to provide, I am sure that you will have an idea on how these algorithm work and what are the things to be done in order to find them.
My only hope after this is that, you will make this helpful post as one of your springboard (if any) for you to understand and learn deeper on what data mining is all about.
So let us now cut the introduction short and get started. Here are the six of the best algorithms:
1. The AdaBoost
What does this do?
From the name itself, AdaBoost is basically an algorithm boosting that construct a classifier. If you recall when we talk about classifier, it usually takes bunch of information and tries to predict the classification of the set of data.
So what is boosting? It refers to an ensemble type of algorithm learning that can take multiple algorithm in learn and effectively combines all of them. The goal here is to ensemble the weak learners and combines it in order to create a strong single one.
Now the difference between weak and strong learner, a learner that is weak classifies with accuracy above chance. The best example for this one is decision stump that is one step ahead on a decision tree.
A strong learn has higher accuracy, and it is used often, one example of it is SVM.
We will share to you a concrete example for AdaBoost. Now, assuming that we have three weak learners, we are then using them in a ten round dataset training that contains a patient data.
Of course the dataset has data on the medical records of the patients. Now the big question is how to effectively predict if a certain patient in the future will have cancer.
Here is how AdaBoost respond that query.
Round 1: AdaBoost will take the sample of the dataset and process attesting to determine the accurateness of the learner. The main goal for this is to determine the best information or learner.
Round 2: AdaBoost repeatedly attempts to assess for the best item or learner. And now here is the kicker, similar to the training data of the patient, the data is now predisposed by heavily weights that are misclassified, and also those who are previously misclassified patient will have higher chances of being chosen as sample.
It is similar to video game wherein you want to move to the next level by not starting at the first level since you are killed. Instead you immediately start at the second level and focus your energy to move to level 3 and so on.
In the same manner, the first batches of classified learner are already set. So instead of just classifying them again, the focus now is on the misclassified patients. The learners that were tagged as beset will be incorporated in the ensemble.
The misclassified patients are then weighted so they will have a chance of being picked as the system repeat and rinse the process.
Therefore at the end of the ten rounds, the process will weight learners and repeatedly reprocess those who are misclassified until such time that data that failed to be classified on the previous round will be picked.
Now is this an unsupervised or supervised learning? AdaBoost is a supervised type of learning, since the iteration will train the weaker learner identified in the dataset. In addition to that, AdaBoost is amazingly fast and simpler, meaning it can execute process much faster. It is also elegant for auto-tuning your classifier, since running successive AdaBoost round will refine weight to classify learners.
Our final word, Adaboost is amazingly versatile yet flexible. It can incorporate many learning algorithm and can work in large array of dataset.
2. The K-Means
What does this do?
To discuss further, k-means creation of k groups from the set of collected objects. Therefore with the classification of objects, we can picture out that the members of the group are obviously similar. It is also a popular cluster analysis method for exploring dataset.
Just hang on. We know what is on your mind. What is a cluster analysis? When we talk about cluster analysis, this is merely an algorithm specially designed to function as forming groups in which the members of the group are similar compared to the non group members(we believed the definition is clear then).
Both groups and clusters are indentified as synonymous in cluster analysis world.
Do these have example? Without doubt, let us assume that we have patient’s dataset. In the cluster analysis, it is categorized as observation.
Of course we know many things about the patients like the age, blood pressure, pulse, cholesterol, VO2max, etc. we identified these items as “attributes” and these are the vectors that clearly represent each of the patient.
Let us look deeper, we can think basically of vector as list of items that will describe a patient. The said list can be interpreted like coordinates in a multi-dimensional hole. Take note, a patient’s pulse can be a single dimension, the blood pressure on the other hand can represent another unique dimension and this will continue until such data will be interpreted.
You might wonder that given with these attributes, how we are going to cluster them all together in similarity like age, the pulse, the blood pressure, etc.
The best part is that, K-means the cluster you want, and k-means take cares the rest. But how k-means take cares the rest? K-means has many variations in order to effectively optimize certain types of processed data.
Basing on high level, they do same things. For k-means it picks up the important points in the multi dimensional area in order to represent these points in k clusters. These are also labeled as “centroids”.
Every single patient that is closes to the k centroids and not all of them will be having the same points as a close to the centroids. They simply form a cluster those who are within the range of the centroids.
The idea we have above now points out about the k clusters and every patient according to range becomes a member of the created cluster. With k-means it then finds the center for every created k clusters based on cluster members and of course using patient’s attributes.
After getting the center point, it then becomes the newly created centroid for every cluster. Therefore the centroid will become a different place right now. The patients who are much closer to other created centroids will have the option to change its cluster membership.
The steps two and three are then repeated until such time these centroids becomes stable, like k membership stabilize. When that thing happens that is now called “convergence”.
Now is the vector unsupervised or supervised?
These actually depend, but k-means usually classified as k-means. Other than just specifying the clusters, when it talks about k-means it talks about learning the cluster automatically based on the guidelines without any data in which will address the clustering.
Why do we need to use k-means?
But we believed that this will not be an issue. The key point of what really k-means mean is simplicity. When we talk about simplicity, it means faster and efficient compared to other algorithms in handling large data sets.
In addition, k-means can also handle massive pre clustered dataset followed by expensive cluster data analysis on sub clusters.
With k-means, it can also be used to rapidly process a k set and explore whether there are missed patterns or dataset relations. But, with all the strong points that we have said, we also found out two weaknesses for k-means. This is about sensitivity to the outliers and sensitivity to centroid choices.