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differences between clusters on variables that were not used in the cluster analysis. It is critical for the validity of the test that the variable not be used in forming the clusters. A different type of external analysis is based on a data partition generated independently of the data set at hand. The partition can be speci ed from a theoretical model or obtained from a clustering of a separate data set. Hubert and Baker (1977) developed a method to test for the signi cance of similarity between the two sets of partitions. The test is based on an assumption of independent assignments to groups in the two partition sets. It is important to note that the Hubert and Baker method cannot be applied to two clusterings of the same data set. Doing so would not result in two independent groupings of the objects in the study. An internal criterion analysis is based on information obtained from within the clustering process. These analyses are based on measures that attempt to represent in some form the goodness of t between the input data and the resulting cluster partitions. There are numerous ways in which to measure the goodness of t. Milligan (1981b) conducted a study of 30 internal criterion indices for cluster analysis. For an extended discussion of such indices, see Milligan (1981b). Milligan s research indicated that indices such as the gamma, C-index, and tau measures should make an effective measure of internal consistency. The advantage to identifying an effective internal criterion index is that it can serve as a test statistic in a hypothesistesting context. The test can be used to determine whether a signi cant clustering exists in the data. The main problem with this approach is the speci cation of a suitable sampling distribution for the test statistic under the null hypothesis of no cluster structure. One can use randomization methods, or bootstrapping, to generate an approximate sampling distribution. Milligan and Sokol (1980), Begovich and Kane (1982), and Good (1982) have all proposed tests based on this strategy. Unfortunately, software support for this form of testing is not widely available. Replication Analysis Replication analysis within a clustering context appears to have been developed by McIntyre and Blash eld (1980) and by Morey, Blash eld, and Skinner (1983). Replication analysis is analogous to a cross-validation procedure in multiple regression. The logic behind replication analysis is that if an underlying clustering exists in the data set, then one should be able to replicate these results in a second sample from the same source and set of variables. There are six steps in a replication analysis. First, one obtains two samples. This can be done by taking a random split-half reliability of a larger

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All of these protocols could run over Ethernet, Token Ring, or FDDI. In this respect, they did openly support the LAN protocol. However, disregarding the LAN protocol, these protocols were proprietary; in other words, vendor dependent. However, other protocols beyond TCP/IP are proprietary, and the internals of those systems are known only to their respective company owners. Users and network administrators were held to proprietary network environments and proprietary network applications, which deterred network development and enhancement in all corporate environments. Just because a vendor supported XNS, did not mean that it would interoperate with other vendors running XNS. Running XNS on one system did not guarantee compatibility of

In a sense, all of its les are ushed down the digital drain. But be careful. If those les contain sensitive information, they can come back to bite you. A le is like a road-sign that tells Windows where to locate some data on your hard drive. When you delete a le and empty it from the Recycle Bin, you are destroying only the road-sign not the actual data. That means the data is just sitting in limbo on your hard drive until it is overwritten by new data.

Double-click the My Computer icon on your desktop. If this icon is not available, click the Start button in the lower-left corner of Windows and click My Computer. If you can t nd the My Computer icon anywhere, do the following: a. Right-click in the empty space on your desktop. b. Select Properties. c. A window opens. Click the Desktop tab. d. Near the bottom of the window, click the Customize Desktop button. e. Another window opens. On the General tab, beneath the words Desktop Icons, place a checkmark in the My Computer box. f. Click OK. g. You are returned to the previous screen. Click Apply. h. Click OK. i. The My Computer icon appears on your desktop. Double-click it.

Adjoint of a matrix: For m 2: 2, the (m x m) matrix Aatij [cof(al})l' is the adjoint of the (m x m) matrix A :::: [ail)' Here cof(aij) is the