ing which overlaps the energy from hard interactions, and should not consume too much computer resources such as CPU. Finally, the algorithm must be completely specified to avoid different interpretations. Commonly used jet-finding algorithms can be divided into two categories: (a) cone clus- tering and (b) pair-wise recombination algorithms. With a few exceptions, only cone clus- tering algorithms have been used at hadron colliders. The cone clustering algorithms used prior to the Tevatron Run II were not IR/collinear safe [26, 27], and it was proposed to add an additional seed at the midpoint of stable cones. This made the new algorithm IR/collinear safe to NLO for the inclusive jet cross section measurement. For other physics observable, it is either safe at LO only or unsafe at all orders [28]. Various issues related to jet reconstruction are extensively discussed in a recent review [29]. For jet studies in Run II, both the CDF and DØ collaborations are using the Midpoint algorithm, as laid out by the QCD Workshop recommendations [26], but the two implemen- tations differ in some details. Below we describe the implementation of this algorithm by the CDF collaboration. The clustering process starts by making a list of all objects to be clustered. In simulated events, all the particles or partons are included without a p$_{T}$ threshold. However, in data, the calorimeter towers are required to have p$_{T}$ ≥ 100 MeV /c to minimize the effect of noise. From this list, a second list of seed objects is made with the requirement that the p$_{T}$ of the objects exceeds a fixed threshold of 1.0 GeV /c . At each seed location, the 4-momentum of the cluster is determined by summing the 4-momenta of all the objects within a distance R = √ ( y − y$_{c}$ ) 2 + ( φ − φ$_{c}$ ) 2 from the seed ( y$_{c}$, φ$_{c}$ ). The 4-momenta are summed using the E-scheme [26], A. Cone Clustering Algorithm ( E, p$_{x}$, p$_{y}$, p$_{z}$ ) = ∑ i ( E, p$_{x}$, p$_{y}$, p$_{z}$ )$_{i}$ (2) p$_{T}$ = √ p 2 x + p 2 y y$_{c}$ = 1 2 ln ( E + p$_{z}$ E − p$_{z}$ ) φ$_{c}$ = tan − $^{1}$( p$_{y}$/p$_{x}$ ) . (3) This scheme is different from the Snowmass scheme [30] used in Run I, where the clustering centroid was defined as the E$_{T}$ -weighted average of η and φ . Using the center of the cluster 10