Question

introduction to data mining:

5. What is the time and space complexity of fuzzy c-means? Of SOM? How do
these complexities compare to those of K-means?
The time complexity of K-means O(I ∗ K ∗ m ∗ n), where I is the number
of iterations required for convergence, K is the number of clusters, m is the
number of points, and n is the number of attributes. The time required by
fuzzy c-means is basically the same as K-means, although the constant is
much higher. The time complexity of SOM is also basically the same as Kmeans
because it consists of multiple passes in which objects are assigned to
centroids and the centroids are updated. However, because the surrounding
centroids are also updated and the number of passes can be large, SOM will
typically be slower than K-means.

Answer 1

The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as "knowledge discovery in databases," the term "data mining" wasn’t coined until the 1990s. But its foundation comprises three intertwined scientific disciplines: statistics (the numeric study of data relationships), artificial intelligence (human-like intelligence displayed by software and/or machines) and machine learning (algorithms that can learn from data to make predictions). What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power.

Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time-consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Retailers, banks, manufacturers, telecommunications providers and insurers, among others, are using data mining to discover relationships among everything from pricing, promotions and demographics to how the economy, risk, competition and social media are affecting their business models, revenues, operations and customer relationships.

Who is using it?
1. Banking
2. Insurance
3. Retail
4. Education
5. Manufacturing