Information Systems homework help.
1. Suppose that you are employed as a data mining consultant for an Internet search engine company. Describe how data mining can help the company by giving specific examples of how techniques, such as clustering, classification, association rule mining, and anomaly detection can be applied.
2. Identify at least two advantages and two disadvantages of using color to visually represent information.
3. Consider a group of documents that has been selected from a much larger set of diverse documents so that the selected documents are as dissimilar from one another as possible. If we consider documents that are not highly related (connected, similar) to one another as being anomalous, then all of the documents that we have selected might be classified as anomalies. Is it possible for a data set to consist only of anomalous objects or is this an abuse of the terminology?
4.For sparse data, discuss why considering only the presence of non-zero values might give a more accurate view of the objects than considering the actual magnitudes of values. When would such an approach not be desirable?
5.Assume that all documents have been normalized to have unit length of 1. What is the “shape” of a cluster that consists of all documents whose cosine similarity to a centroid is greater than some specified constant? In other words, cos(d, c) ≥ δ, where 0 < δ ≤ 1.
Note: Each answer should be 300-350 words, No plagiarism, Needs to completed by Wednesday (08/19)7 PM PST.
The Textbook attached for reference.
2. Identify at least two advantages and two disadvantages of using color to visually represent information.
3. Consider a group of documents that has been selected from a much larger set of diverse documents so that the selected documents are as dissimilar from one another as possible. If we consider documents that are not highly related (connected, similar) to one another as being anomalous, then all of the documents that we have selected might be classified as anomalies. Is it possible for a data set to consist only of anomalous objects or is this an abuse of the terminology?
4.For sparse data, discuss why considering only the presence of non-zero values might give a more accurate view of the objects than considering the actual magnitudes of values. When would such an approach not be desirable?
5.Assume that all documents have been normalized to have unit length of 1. What is the “shape” of a cluster that consists of all documents whose cosine similarity to a centroid is greater than some specified constant? In other words, cos(d, c) ≥ δ, where 0 < δ ≤ 1.
Note: Each answer should be 300-350 words, No plagiarism, Needs to completed by Wednesday (08/19)7 PM PST.
The Textbook attached for reference.
-
Introduction-to-Data-Mining-2nd-Edition-by-Pang-Ning-Tan.pdf.pdf
